From 461e9e8b08dda3f9a9e89f2075fabe1bcb97c67d Mon Sep 17 00:00:00 2001
From: Philip Monaco <philmonaco34@gmail.com>
Date: Thu, 17 Feb 2022 12:21:22 -0500
Subject: [PATCH] add updated README.md
---
README.md | 29 +-
image_preprocessing.ipynb | 997 --------------------------------------
2 files changed, 23 insertions(+), 1003 deletions(-)
delete mode 100644 image_preprocessing.ipynb
diff --git a/README.md b/README.md
index 107d638..8ff3021 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,26 @@
# Introduction
Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc).
+
+
# Prerequisites for Use
-1. Download the training data [Here](https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task3_Training_Input.zip)
-2. Download the training ground truth [Here](https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task3_Training_GroundTruth.zip)
-3. Download the test data images [Here](https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task3_Validation_Input.zip)
-4. Download the test data ground truth [Here](https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task3_Validation_GroundTruth.zip)
-5. Unzip the files into the data folder in this repository.
-6. Download required packages from requirements.txt, "pip install -r requirements.txt"
\ No newline at end of file
+
+All of the data will need to be placed in directory:
+
+`hvm-image-clf/data`
+
+First we will need to download 3 data files used by the package. The first two can be downloaded by clicking the links below.
+
+[Training Data](https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task3_Training_Input.zip) - Unzip and place the folder named "ISIC2018_TASK3_Training_Input" and place in the data directory. This contains all of the training images.
+
+[Ground Truth](https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task3_Training_GroundTruth.zip) - Unzip and place the file named "ISIC2018_Task3_Training_GroundTruth.csv". This contains all of our ground truth labels of the training images.
+
+The third dataset can be downloaded by following this link
+[Metadata](https://dataverse.harvard.edu/file.xhtml?fileId=4338392&version=3.0). This will bring you to a website called Harvard Dataverse. On the page you will be able to see dropdown box called "Access File". Select the option called "Comma Separated Values (Original File Format) to download the dataset.
+
+Project dependencies can be installed using
+
+`pip install -r requirements.txt`
+
+# Running the notebook.
+
+If all of the pre-requisites are setup correctly, the notebook file can be run using an IPython or Anaconda distributed notebook ide.
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diff --git a/image_preprocessing.ipynb b/image_preprocessing.ipynb
deleted file mode 100644
index 08f64d7..0000000
--- a/image_preprocessing.ipynb
+++ /dev/null
@@ -1,997 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "441343d0-e422-4dae-b988-9130e5a0d565",
- "metadata": {},
- "source": [
- "## Packages\n",
- "os: Operating system interface\n",
- "\n",
- "OpenCV: cv2 `pip install opencv-python>=4.5.5`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "d7e56e0e-7eec-429d-940b-c3337db4b4dc",
- "metadata": {},
- "outputs": [],
- "source": [
- "%load_ext autoreload\n",
- "%autoreload 2\n",
- "import os\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import tensorflow as tf\n",
- "import importlib as lib\n",
- "from data_processing import load_sort_data, transform\n",
- "import EDA\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "caa99aac",
- "metadata": {},
- "source": [
- "# Introduction\n",
- "The project we are presenting is a multi-label image classification task based on the 2018 Human vs Machine skin lesion analysis toward melanoma detection hosted by the International Skin Imaging Collaboration (ISIC).\n",
- "\n",
- "This notebook will contain the following sections:\n",
- " 1. Problem Definition & Data Description\n",
- " 2. Data Preparation\n",
- " 3. Exploratory Analysis\n",
- " 4. Data Processing for Model Ingestion\n",
- " 5. Model Creation\n",
- " 6. Model Scoring & Evaluation\n",
- " 7. Interpretation of Results"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "830dee53",
- "metadata": {},
- "source": [
- "# 1. Problem Definition & Data description\n",
- "\n",
- "#### Problem Definition:\n",
- "Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. With a sufficiently large and diverse collection of skin lesions we will develop a method to automate the prediction of disease classification within dermoscopic images. The project is meant to human computer computer collaboration and not intended to replace traditional forms of diagnosis. \n",
- "\n",
- "Possible disease categories (and abbreviation) for classification are:\n",
- " 1. Melanoma (mel)\n",
- " 2. Melanocytic Nevus (nv)\n",
- " 3. Basal Cell Carcinoma (bcc)\n",
- " 4. Actinic Keratosis / Bowen's Disease (akiec)\n",
- " 5. Benign Keratosis (bkl)\n",
- " 6. Dermatofibroma (df)\n",
- " 7. Vascular Lesion (vasc)\n",
- "\n",
- "#### Data Description\n",
- "- Data images are in JPEG format using the naming scheme `ISIC_.jpg` where _ is a 7 digit unique identifier of the image.\n",
- "- There are a total of 10,015 600x450 pixel color images contained in the training data folder.\n",
- "- There are a total of 193 600x450 pixel color images contained in the validation data folder.\n",
- "- The training metadata is a 10,015x8 .csv file containing the following variables*:\n",
- " - lesion_id: Unique identifier of a legion with multiple images.\n",
- " - image_id: Unique identifier of the associated image file.\n",
- " - dx: Prediction label containing the 7 abbreviated disease categories.\n",
- " - dx_type: Method of how the diagnosis was confirmed. \n",
- " - age: Numeric year age of the patient.\n",
- " - sex: String binary value 'male' or 'female'.\n",
- " - localization: Categorical location on the body the image was taken. \n",
- " - dataset: Image source.\n",
- "\n",
- "*Further details of the data will be provided in the data preparation section."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "96ff082e",
- "metadata": {},
- "source": [
- "# 2. Data Preparation\n",
- "\n",
- "#### Step 1. Load and Sort\n",
- "First we will load the data using the function `load_sort_data()`.\n",
- "\n",
- "The `load_sort_data()` function sorts the images into folders based on the diagnosis label. This will help reduce the overall size of the dataset and make preprocessing the images much faster. The function will return the metadata as a pandas DataFrame and the path of the sorted image folders. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "b8c4f292",
- "metadata": {},
- "outputs": [],
- "source": [
- "# function takes 3 parameters: metadata filename, the folder of the raw images, and the desired name of the destination directory. \n",
- "metadata, dest_dir = load_sort_data('HAM10000_metadata', 'ISIC2018_Task3_Training_Input', 'Training_Images_')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "7e9702c3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'c:\\\\Users\\\\Bennett Nolan\\\\Desktop\\\\info442\\\\hvm-image-clf/data/Training_Images_'"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# The path of our training image folders sorted by label\n",
- "dest_dir"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "20b8415f",
- "metadata": {},
- "source": [
- "#### Step 2. Tidy Metadata\n",
- "We will now take steps to tidy our metadata.\n",
- "First, subset the variables we intend on using, next analyze missingness and finally we will correct our expected datatypes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "0ba9148a",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.frame.DataFrame'>\n",
- "RangeIndex: 10015 entries, 0 to 10014\n",
- "Data columns (total 5 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 image_id 10015 non-null object \n",
- " 1 dx 10015 non-null object \n",
- " 2 age 9958 non-null float64\n",
- " 3 sex 10015 non-null object \n",
- " 4 localization 10015 non-null object \n",
- "dtypes: float64(1), object(4)\n",
- "memory usage: 391.3+ KB\n"
- ]
- }
- ],
- "source": [
- "# Subsetting into variables we will use. \n",
- "metadata = metadata[['image_id', 'dx', 'age', 'sex', 'localization']]\n",
- "# We will need to change the Dtypes of the columns into the expected types\n",
- "metadata.info()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3f4a3578",
- "metadata": {},
- "source": [
- "As we can see below, we have a total of 57 NA values in age. When looking at the distribution of NA values, only our largest quantity of labels have NA's. The age variable is only useful in providing context to our problem and will not be used as a feature in our model. Therefore it is not necessary to do anything further to the NA values. During exploratory analysis we can deal with the NA values as needed."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "e6d378d5",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Total number of unique labels\n",
- " nv 6705\n",
- "mel 1113\n",
- "bkl 1099\n",
- "bcc 514\n",
- "akiec 327\n",
- "vasc 142\n",
- "df 115\n",
- "Name: dx, dtype: int64 \n",
- "Number of NaN values within each label\n",
- " dx image_id age sex localization\n",
- "0 akiec 0 0 0 0\n",
- "1 bcc 0 0 0 0\n",
- "2 bkl 0 10 0 0\n",
- "3 df 0 0 0 0\n",
- "4 mel 0 2 0 0\n",
- "5 nv 0 45 0 0\n",
- "6 vasc 0 0 0 0\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\Bennett Nolan\\AppData\\Local\\Temp\\ipykernel_10700\\2944415004.py:5: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
- " metadata.drop('dx',1).isna().groupby(\n"
- ]
- }
- ],
- "source": [
- "# Sum the na values contained within each label\n",
- "print(\"Total number of unique labels\\n\",\n",
- " metadata['dx'].value_counts(), \n",
- " \"\\nNumber of NaN values within each label\\n\",\n",
- " metadata.drop('dx',1).isna().groupby(\n",
- " metadata.dx, \n",
- " dropna=False, \n",
- " observed = True\n",
- " ).sum().reset_index()\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "91aa284b",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.frame.DataFrame'>\n",
- "RangeIndex: 10015 entries, 0 to 10014\n",
- "Data columns (total 5 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 image_id 10015 non-null string \n",
- " 1 dx 10015 non-null category\n",
- " 2 age 9958 non-null float64 \n",
- " 3 sex 10015 non-null category\n",
- " 4 localization 10015 non-null category\n",
- "dtypes: category(3), float64(1), string(1)\n",
- "memory usage: 187.1 KB\n"
- ]
- }
- ],
- "source": [
- "#Changing datatypes\n",
- "dtypes = {'image_id':'string', \n",
- " 'dx':'category', \n",
- " 'sex':'category',\n",
- " 'localization': 'category'\n",
- " }\n",
- "metadata = metadata.astype(dtypes)\n",
- "metadata.info()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "41f467b5",
- "metadata": {},
- "source": [
- "#### Step 3. Image Processing\n",
- "\n",
- "In this step we will construct an NxM matrix where N is an image and M is the number of pixels in the image. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "05398a91",
- "metadata": {},
- "outputs": [],
- "source": [
- "#Assign vectorized images to variables\n",
- "akiec_images = transform(dest_dir + 'akiec')\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "e8642d8d",
- "metadata": {},
- "outputs": [],
- "source": [
- "bcc_images = transform(dest_dir + 'bcc')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "5312b5de",
- "metadata": {},
- "outputs": [],
- "source": [
- "bkl_images = transform(dest_dir + 'bkl')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "49338970",
- "metadata": {},
- "outputs": [],
- "source": [
- "df_images = transform(dest_dir + 'df')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "784d69cd",
- "metadata": {},
- "outputs": [],
- "source": [
- "mel_images = transform(dest_dir + 'mel')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "6cd167a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "#This takes a really long time to run even when cutting down the images size.\n",
- "nv_images = transform(dest_dir + 'nv', size=(200, 150))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "4de5cec3",
- "metadata": {},
- "outputs": [],
- "source": [
- "vasc_images = transform(dest_dir + 'vasc')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "d92158fa",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1113, 67500)"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mel_images.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3fb5f03e",
- "metadata": {},
- "source": [
- "# 3. Exploritory Data Analysis\n",
- "\n",
- "Exploritory analysis will be conducted on in two major steps. First we will complete analysis on the metadata then the image dataset. "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6ca3d6bf",
- "metadata": {},
- "source": [
- "#### Step 1: Metadata EDA\n",
- "We will perform the following analysis on the metadata:\n",
- "- Summary Statistics\n",
- "- Class label distributions\n",
- "- Correlation"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "686965dd",
- "metadata": {},
- "source": [
- "Summary Statistics"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "5d475aed",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
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- " .dataframe thead th {\n",
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- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>image_id</th>\n",
- " <th>dx</th>\n",
- " <th>age</th>\n",
- " <th>sex</th>\n",
- " <th>localization</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>ISIC_0027419</td>\n",
- " <td>bkl</td>\n",
- " <td>80.0</td>\n",
- " <td>male</td>\n",
- " <td>scalp</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>ISIC_0025030</td>\n",
- " <td>bkl</td>\n",
- " <td>80.0</td>\n",
- " <td>male</td>\n",
- " <td>scalp</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>ISIC_0026769</td>\n",
- " <td>bkl</td>\n",
- " <td>80.0</td>\n",
- " <td>male</td>\n",
- " <td>scalp</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>ISIC_0025661</td>\n",
- " <td>bkl</td>\n",
- " <td>80.0</td>\n",
- " <td>male</td>\n",
- " <td>scalp</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>ISIC_0031633</td>\n",
- " <td>bkl</td>\n",
- " <td>75.0</td>\n",
- " <td>male</td>\n",
- " <td>ear</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " image_id dx age sex localization\n",
- "0 ISIC_0027419 bkl 80.0 male scalp\n",
- "1 ISIC_0025030 bkl 80.0 male scalp\n",
- "2 ISIC_0026769 bkl 80.0 male scalp\n",
- "3 ISIC_0025661 bkl 80.0 male scalp\n",
- "4 ISIC_0031633 bkl 75.0 male ear"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metadata.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "6e579e93",
- "metadata": {},
- "outputs": [
- {
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- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>age</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>85.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>median</th>\n",
- " <td>50.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>51.863828</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>skew</th>\n",
- " <td>-0.166802</td>\n",
- " </tr>\n",
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- "</div>"
- ],
- "text/plain": [
- " age\n",
- "min 0.000000\n",
- "max 85.000000\n",
- "median 50.000000\n",
- "mean 51.863828\n",
- "skew -0.166802"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metadata.agg({\n",
- " \"age\":[\"min\", \"max\", \"median\", \"mean\", \"skew\"] \n",
- "})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "ea361300",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "sex\n",
- "female 4552\n",
- "male 5406\n",
- "unknown 57\n",
- "Name: sex, dtype: int64"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metadata.groupby(\"sex\")[\"sex\"].count()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e031ebf0",
- "metadata": {},
- "source": [
- "Class Label Distributions"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bf51add8",
- "metadata": {},
- "source": [
- "Distributions for metadata including Age, Localization, Sex, and Diagnosis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "6681e88c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Localization')"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1080x1080 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig =plt.figure(figsize=(15,15))\n",
- "ax1 = fig.add_subplot(221)\n",
- "metadata['sex'].value_counts().plot(kind='bar', ax=ax1)\n",
- "ax1.set_title('Sex')\n",
- "\n",
- "ax2=fig.add_subplot(222)\n",
- "metadata['localization'].value_counts().plot(kind='bar', ax=ax2)\n",
- "ax2.set_title('Localization')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "f6596829",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Diagnosis')"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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KmJmRcjnw8YHyo/pZLQ8A7ui7Y54NHJRkx37my4P6MkmSJEnSEDY6m2WSDwLPBHZJcgPdrJQrgTOSHA1cBxzR734W8FxgLXAn8FKAqlqf5I3ABf1+b6iq2ZOqSJIkSZI20UbDXFW9eI5NB25g3wKOnaOek4CTNqt1ug+XO5AkSZIEY5gARZKkLVmSa5NcluSSJGv6sp2SrE5ydf97x748Sd6ZZG2SS5PsO1DP8n7/q5Msn+v5JEmaYZiTJGl0v1ZVT66qZf39FcA5VbU3cE5/H+BQYO/+5xjgPdCFP7phDPsD+wHHzQRASZLmYpiTJGn8DgdO7m+fDLxgoPyU6pwL7NDPCn0wsLqq1lfVbcBq4JB5brMkqTGGOUmSRlPAp5NcmOSYvmzXfjZngJuBXfvbuwPXDzz2hr5srvJ7cS1WSdKgjU6AIkmS7tevVNWNSR4GrE5y1eDGqqokNY4nqqoTgBMAli1bNpY6JUnt8sqcJEkjqKob+9+3Ah+jG/N2S999kv73rf3uNwJ7Djx8j75srnJJkuZkmJMkaUhJHpzkITO3gYOArwKrgJkZKZcDH+9vrwKO6me1PAC4o++OeTZwUJId+4lPDurLJEmak90sJUka3q7Ax5JA95n6gar6VJILgDOSHA1cBxzR738W8FxgLXAn8FKAqlqf5I3ABf1+b6iq9fN3GJKkFhnmJEkaUlVdAzxpA+XfAQ7cQHkBx85R10nASeNuoyRp8bKbpSRJkiQ1yDAnSZIkSQ0yzEmSJElSgwxzkiRJktQgw5wkSZIkNcgwJ0mSJEkNMsxJkiRJUoMMc5IkSZLUIMOcJEmSJDXIMCdJkiRJDTLMSZIkSVKDDHOSJEmS1CDDnCRJkiQ1yDAnSZIkSQ0yzEmSJElSgwxzkiRJktQgw5wkSZIkNcgwJ0mSJEkNMsxJkiRJUoMMc5IkSZLUIMOcJEmSJDXIMCdJkiRJDTLMSZIkSVKDDHOSJEmS1CDDnCRJkiQ1yDAnSZIkSQ0yzEmSJElSgwxzkiRJktQgw5wkSZIkNcgwJ0mSJEkNMsxJkiRJUoMMc5IkSZLUIMOcJEmSJDXIMCdJkiRJDTLMSZIkSVKDDHOSJEmS1KCRwlyS/5nk8iRfTfLBJL+QZK8k5yVZm+T0JNv2+27X31/bb186liOQJEmSpC3Q0GEuye7AnwLLquoJwNbAkcBbgbdX1aOB24Cj+4ccDdzWl7+930+SJEmSNIRRu1luAzwwyTbAg4CbgGcBH+63nwy8oL99eH+ffvuBSTLi80uSJEnSFmmbYR9YVTcm+Rvgm8APgU8DFwK3V9Vd/W43ALv3t3cHru8fe1eSO4CdgW8P2waNx9IVZ27yvteuPGyCLZEkSZK0qUbpZrkj3dW2vYBHAA8GDhm1QUmOSbImyZp169aNWp0kSZIkLUqjdLN8NvCNqlpXVT8FPgo8A9ih73YJsAdwY3/7RmBPgH779sB3ZldaVSdU1bKqWrZkyZIRmidJkiRJi9coYe6bwAFJHtSPfTsQuAL4HPBb/T7LgY/3t1f19+m3f7aqaoTnlyRJkqQt1tBhrqrOo5vI5CLgsr6uE4BXA3+WZC3dmLgT+4ecCOzcl/8ZsGKEdkuStCAk2TrJxUk+2d/f7CV6krymL/9akoOndCiSpMYMPQEKQFUdBxw3q/gaYL8N7Psj4IWjPJ8kSQvQy4ErgYf292eW6Dktyd/TLc3zHgaW6Ekys5TPi5LsQ7e0z+PpxqB/Jsljqupn830gkqS2jLo0gSRJW6wkewCHAe/t74fNX6LncOC0qvpxVX0DWMsGTopKkjSbYU6SpOG9A3gV8PP+/s5s4hI9wMwSPXeXb+AxkiTNyTAnSdIQkjwPuLWqLpzH53T5HknS3QxzkiQN5xnA85NcC5xG173yeDZ/iZ67yzfwmHtx+R5J0iDDnCRJQ6iq11TVHlW1lG4Ck89W1UvY/CV6VgFH9rNd7gXsDZw/T4chSWrYSLNZSpKk+3g1cFqSNwEXc+8lev6pX6JnPV0ApKouT3IG3VqtdwHHOpOlJGlTGOYkSRpRVX0e+Hx/e7OX6KmqNwNvnlwLJUmLkWFOE7F0xZmbvO+1Kw+bYEskSZKkxckwp6Zsakg0IEqSJGmxcwIUSZIkSWqQYU6SJEmSGmSYkyRJkqQGGeYkSZIkqUGGOUmSJElqkLNZaovnDJmSJElqkVfmJEmSJKlBhjlJkiRJapBhTpIkSZIaZJiTJEmSpAYZ5iRJkiSpQYY5SZIkSWqQYU6SJEmSGmSYkyRJkqQGGeYkSZIkqUGGOUmSJElqkGFOkiRJkhpkmJMkSZKkBhnmJEmSJKlBhjlJkiRJapBhTpIkSZIaZJiTJEmSpAYZ5iRJkiSpQYY5SZIkSWqQYU6SJEmSGmSYkyRJkqQGGeYkSZIkqUGGOUmSJElqkGFOkiRJkhpkmJMkSZKkBhnmJEmSJKlBhjlJkiRJapBhTpIkSZIaZJiTJEmSpAYZ5iRJkiSpQdtMuwHSYrR0xZmbtN+1Kw+bcEskSZK0WHllTpIkSZIaNFKYS7JDkg8nuSrJlUmenmSnJKuTXN3/3rHfN0nemWRtkkuT7DueQ5AkSZKkLc+oV+aOBz5VVY8DngRcCawAzqmqvYFz+vsAhwJ79z/HAO8Z8bklSZIkaYs1dJhLsj3wX4ETAarqJ1V1O3A4cHK/28nAC/rbhwOnVOdcYIckuw37/JIkSZK0JRvlytxewDrgfUkuTvLeJA8Gdq2qm/p9bgZ27W/vDlw/8Pgb+rJ7SXJMkjVJ1qxbt26E5kmSJEnS4jVKmNsG2Bd4T1U9BfgB93SpBKCqCqjNqbSqTqiqZVW1bMmSJSM0T5IkSZIWr1HC3A3ADVV1Xn//w3Th7paZ7pP971v77TcCew48fo++TJKkJiX5hSTnJ/lKksuTvL4v3yvJef2kX6cn2bYv366/v7bfvnSgrtf05V9LcvCUDkmS1JChw1xV3Qxcn+SxfdGBwBXAKmB5X7Yc+Hh/exVwVD+r5QHAHQPdMSVJatGPgWdV1ZOAJwOH9J9xbwXeXlWPBm4Dju73Pxq4rS9/e78fSfYBjgQeDxwCvDvJ1vN5IJKk9ow6m+WfAKcmuZTuQ+wtwErgOUmuBp7d3wc4C7gGWAv8I/DHIz63JElT1U/q9f3+7gP6nwKeRddjBe47GdjMJGEfBg5Mkr78tKr6cVV9g+6zcr/JH4EkqWXbjPLgqroEWLaBTQduYN8Cjh3l+SRJWmj6K2gXAo8G3gV8Hbi9qu7qdxmc8OvuycCq6q4kdwA79+XnDlQ75yRhdMv78MhHPnLsxyJJasuoV+YkSdqiVdXPqurJdGPB9wMeN8HncpIwSdLdDHOSJI1Bv9bq54Cn062lOtP7ZXDCr7snA+u3bw98BycJkyQNwTAnSdKQkixJskN/+4HAc4Ar6ULdb/W7zZ4MbGaSsN8CPtsPQ1gFHNnPdrkXsDdw/rwchCSpWSONmZMkaQu3G3ByP25uK+CMqvpkkiuA05K8CbgYOLHf/0Tgn5KsBdbTzWBJVV2e5Ay6WaHvAo6tqp/N87FIkhpjmJMkaUhVdSnwlA2UX8MGZqOsqh8BL5yjrjcDbx53GyVJi5fdLCVJkiSpQYY5SZIkSWqQYU6SJEmSGmSYkyRJkqQGGeYkSZIkqUGGOUmSJElqkGFOkiRJkhpkmJMkSZKkBhnmJEmSJKlBhjlJkiRJapBhTpIkSZIaZJiTJEmSpAYZ5iRJkiSpQYY5SZIkSWqQYU6SJEmSGmSYkyRJkqQGGeYkSZIkqUGGOUmSJElqkGFOkiRJkhpkmJMkSZKkBhnmJEmSJKlBhjlJkiRJapBhTpIkSZIaZJiTJEmSpAYZ5iRJkiSpQYY5SZIkSWrQNtNugKRNs3TFmZu877UrD5tgSyRJkrQQeGVOkiRJkhpkmJMkSZKkBhnmJEmSJKlBhjlJkiRJapBhTpIkSZIaZJiTJEmSpAYZ5iRJkiSpQYY5SZIkSWqQYU6SJEmSGmSYkyRJkqQGGeYkSZIkqUGGOUmSJElqkGFOkiRJkho0cphLsnWSi5N8sr+/V5LzkqxNcnqSbfvy7fr7a/vtS0d9bkmSJEnaUo3jytzLgSsH7r8VeHtVPRq4DTi6Lz8auK0vf3u/nyRJkiRpCCOFuSR7AIcB7+3vB3gW8OF+l5OBF/S3D+/v028/sN9fkiRJkrSZthnx8e8AXgU8pL+/M3B7Vd3V378B2L2/vTtwPUBV3ZXkjn7/b4/YBklDWrrizE3e99qVh02wJZIkSdpcQ1+ZS/I84NaqunCM7SHJMUnWJFmzbt26cVYtSdJYJdkzyeeSXJHk8iQv78t3SrI6ydX97x378iR5Zz9+/NIk+w7Utbzf/+oky6d1TJKkdozSzfIZwPOTXAucRte98nhghyQzV/z2AG7sb98I7AnQb98e+M7sSqvqhKpaVlXLlixZMkLzJEmauLuAV1bVPsABwLFJ9gFWAOdU1d7AOf19gEOBvfufY4D3QBf+gOOA/YH9gONmAqAkSXMZOsxV1Wuqao+qWgocCXy2ql4CfA74rX635cDH+9ur+vv02z9bVTXs80uSNG1VdVNVXdTf/h7dhGC7c+9x4rPHj59SnXPpToDuBhwMrK6q9VV1G7AaOGT+jkSS1KJJrDP3auDPkqylGxN3Yl9+IrBzX/5n3HOWUpKk5vVL7jwFOA/Ytapu6jfdDOza3757/HhvZmz5XOWzn8OhCJKku406AQoAVfV54PP97WvouojM3udHwAvH8XySJC0kSX4R+Ajwiqr67uBkzVVVScbSE6WqTgBOAFi2bJm9WyRpCzeJK3OSJG0xkjyALsidWlUf7Ytv6btP0v++tS+/e/x4b2Zs+VzlkiTNyTAnSdKQ+vVSTwSurKq3DWwaHCc+e/z4Uf2slgcAd/TdMc8GDkqyYz/xyUF9mSRJcxpLN0tJkrZQzwB+B7gsySV92Z8DK4EzkhwNXAcc0W87C3gusBa4E3gpQFWtT/JG4IJ+vzdU1fp5OQJJUrMMc5IkDamqvgRkjs0HbmD/Ao6do66TgJPG1zpJ0mJnN0tJkiRJapBhTpIkSZIaZJiTJEmSpAYZ5iRJkiSpQYY5SZIkSWqQYU6SJEmSGmSYkyRJkqQGGeYkSZIkqUGGOUmSJElqkGFOkiRJkhpkmJMkSZKkBhnmJEmSJKlBhjlJkiRJapBhTpIkSZIaZJiTJEmSpAYZ5iRJkiSpQYY5SZIkSWqQYU6SJEmSGmSYkyRJkqQGGeYkSZIkqUGGOUmSJElq0DbTboCkxWXpijM3ed9rVx42wZZIkiQtbl6ZkyRJkqQGGeYkSZIkqUGGOUmSJElqkGFOkiRJkhpkmJMkSZKkBhnmJEmSJKlBhjlJkiRJapBhTpIkSZIaZJiTJEmSpAYZ5iRJkiSpQdtMuwGStDFLV5y5yfteu/KwCbZEkiRp4fDKnCRJkiQ1yDAnSZIkSQ0yzEmSJElSgwxzkiRJktQgw5wkSZIkNcgwJ0mSJEkNMsxJkiRJUoMMc5IkSZLUoKEXDU+yJ3AKsCtQwAlVdXySnYDTgaXAtcARVXVbkgDHA88F7gR+t6ouGq35kiRJsHTFmfP2XNeuPGzenkuS7s/QYQ64C3hlVV2U5CHAhUlWA78LnFNVK5OsAFYArwYOBfbuf/YH3tP/liRJ0gYYUiXdn6G7WVbVTTNX1qrqe8CVwO7A4cDJ/W4nAy/obx8OnFKdc4Edkuw27PNLkjRtSU5KcmuSrw6U7ZRkdZKr+9879uVJ8s4ka5NcmmTfgccs7/e/OsnyaRyLJKk9Yxkzl2Qp8BTgPGDXqrqp33QzXTdM6ILe9QMPu6EvkySpVe8HDplVtoKuh8rewDn9fbh3D5Vj6Hqo0A9POI6ut8p+wHEzAVCSpPszcphL8ovAR4BXVNV3B7dVVdGNp9uc+o5JsibJmnXr1o3aPEmSJqaqvgisn1W8uT1UDgZWV9X6qroNWM19A6IkSfcxUphL8gC6IHdqVX20L75lpvtk//vWvvxGYM+Bh+/Rl91LVZ1QVcuqatmSJUtGaZ4kSdOwuT1U7LkiSRrK0GGun53yRODKqnrbwKZVwEx//+XAxwfKj+rHDBwA3DHwYSdJ0qIzTA+V+2PvFUnSoFGuzD0D+B3gWUku6X+eC6wEnpPkauDZ/X2As4BrgLXAPwJ/PMJzS5K0UG1uD5VN6rkC9l6RJN3b0EsTVNWXgMyx+cAN7F/AscM+nyRJjZjpobKS+/ZQeVmS0+gmO7mjqm5KcjbwloFJTw4CXjPPbZYkNWiUdeYkSdqiJfkg8ExglyQ30M1KuRI4I8nRwHXAEf3uZwHPpeuhcifwUoCqWp/kjcAF/X5vqKrZk6pIknQfhjlJkoZUVS+eY9Nm9VCpqpOAk8bYNEnSFmAs68xJkiRJkuaXYU6SJEmSGmQ3S0lbrKUrztyk/a5dediEWyJJkrT5vDInSZIkSQ0yzEmSJElSgwxzkiRJktQgw5wkSZIkNcgwJ0mSJEkNMsxJkiRJUoMMc5IkSZLUIMOcJEmSJDXIMCdJkiRJDTLMSZIkSVKDDHOSJEmS1CDDnCRJkiQ1yDAnSZIkSQ0yzEmSJElSgwxzkiRJktQgw5wkSZIkNcgwJ0mSJEkN2mbaDZAkSdKWZ+mKM+ftua5dedi8PZc0n7wyJ0mSJEkNMsxJkiRJUoMMc5IkSZLUIMfMSdIYbeoYEMdvSJKkUXllTpIkSZIaZJiTJEmSpAbZzVKSFji7bkqSpA3xypwkSZIkNcgwJ0mSJEkNspulJEmSNCab2jV+HOxeL6/MSZIkSVKDDHOSJEmS1CDDnCRJkiQ1yDAnSZIkSQ0yzEmSJElSg5zNUpK2QC5ELklS+wxzkqSx2JzpuA2JkiSNzm6WkiRJktQgw5wkSZIkNchulpIkSZLu1+Z0pR8Hu+NvGsOcJEmSpC3WfAbVcYdUu1lKkiRJUoMMc5IkSZLUoHkPc0kOSfK1JGuTrJjv55ckaSHy81GStLnmNcwl2Rp4F3AosA/w4iT7zGcbJElaaPx8lCQNY76vzO0HrK2qa6rqJ8BpwOHz3AZJkhYaPx8lSZstVTV/T5b8FnBIVf1+f/93gP2r6mUD+xwDHNPffSzwtU2sfhfg22Ns7pZe56Tqtc4ts85J1Wudi6vOR1XVkjE/fxM25fOxLx/2M3JUk/p/YSFYrMfmcbVnsR6bxzW6OT8fF9zSBFV1AnDC5j4uyZqqWjbOtmzJdU6qXuvcMuucVL3WuWXWuSUb9jNyVIv5fVysx+ZxtWexHpvHNVnz3c3yRmDPgft79GWSJG3J/HyUJG22+Q5zFwB7J9krybbAkcCqeW6DJEkLjZ+PkqTNNq/dLKvqriQvA84GtgZOqqrLx1T9JLqdbMl1Tqpe69wy65xUvda5Zda56Ez483EcFvP7uFiPzeNqz2I9No9rguZ1AhRJkiRJ0njM+6LhkiRJkqTRGeYkSZIkqUGGOUmSJElqkGFOkiRJkuaQZK9pt2EuTU6AkmR74BBg977oRuDsqrp9odWb5HHA4bPqXFVVV45QZ4D9ZtV5fo3wZjb2mrbyPrXy3jfx9zSJ17Ovd1J/+7sO1llVt4xSX2uS7ARQVeun3RYNJ8mlwGnA6VX19Wm3Z1RJPgHM+f9aVT1/HpujzZDkWODUmf+Xk+wIvLiq3j3Vho0oyVuAv551XK+sqtdNtWEjSvJg4IdV9fP+/lbAL1TVndNt2fCSXFhVT01yTlUdOO32DGouzCU5CjgO+DT3LKi6B/Ac4PVVdcpCqTfJq4EX030Y3jBQ55HAaVW1cog6DwLeDVw9q52PBv64qj49RJ0tvaatvE+tvPdN/D1N4vWcYFufDPw9sP2sOm+ne00vGrKtkzg5MNYgm+SRwF8DB9Idb4CHAp8FVlTVtcO2VfMvyaOAF/U/PwdOB86oqm9OtWFDSvKr97e9qr4wX20ZtyTfowuq4d6BNUBV1UOn0rAxSXJJVT15VtnFVfWUKTVpLDZ0DEkuqqp9p9WmcUhyLvDsqvp+f/8XgU9X1X+ebsuGl+Ri4EPA/wDePnt7Vb1t3hvVazHMfQ3Yf/aXjf5sxnlV9ZiFUm+S/wAeX1U/nVW+LXB5Ve09RJ1XAofO/lLUX/49q6p+eYg6W3pNW3mfWnnvm/h7msTrOcG2XgL8YVWdN6v8AOAfqupJQ9Q5iZMDkwiy/w68A/hwVf2sL9saeCHwiqo6YHPr1MKQZG/gL4CXVNXW027PqJI8EHhkVX1t2m3RxiW5DHjiTI+R/v+VS6vq8dNt2Wj6q99Pq6of9/cfCKxZBMe1ofB9n7KWJHks8ALgFcB76E6UzKiqesMUmgXM86LhYzL7rNOMn3PvF3Yh1Ptz4BHAdbPKd+u3DWMb7vkyN+hG4AFD1tnSa9rK+9TKe9/K39MkXk+YTFsfPDvIAVTVuX3Xk2EczYbD7NuAy4Fhrky+FnjqXEEWGOaK/C5VdfpgQR/qTkvyxiHq05TNujr3M+BV023R6JL8OvA3wLbAXv3V9Dcshm6WSY6uqhNnla2sqhXTatOYfAo4Pck/9Pf/sC9r3anAOUne199/KXDyFNszLj9Isu9MT5QkTwV+OOU2jeow4Kd0vZl+MOW23EuLYe7NwEVJPg1c35c9ku5s8ihfFiZR7yvo/pFePavORwMvG7LOk4ALkpw2UOeedGfoT5zzUfevpde0lfdpEnVO4r1v5e/pFYz/9YTJtPVfk5xJF4YGX9OjGP7LxyTC7CSC7IVJ3k33ZWTw2JcDFw9Zp6YkyXl0J3XOAF5YVddMuUnj8ld044Q/D1BVl2QBT26wmX4zyY+q6lSAJO8CHjjlNo3Dq4Fj6Lq4AawG3ju95oxHVb01yVeAZ/dFb6yqs6fZpjF5BfChJN+i+zx5ON0JoZY9pP/9WOBpwMfpju3XgfOn1ShosJsl3H3m+GDuO87jtoVWbz/oc/bkEhfMdEEass5fZsNjZ64Yoc6WXtNW3qdW3vsm/p4m8XpOsK2HsuHX9Kwh6zsE+Du6sY33CbNVtdkhMcly4C/pulneJ8hW1fuHqHNbuquIg8d+A/AJ4MSZrkRqQz9O86nAoxg4+TvN7kTjkOTcqjpgcLxSkkur6onTbtuo+m56q+hO1B0C3F5VL59uq0bX92r40azu29u1PKHGjP7q995V9ZkkDwK2rqrvTbtdo0ryALrgA/C12T1LWpXki8BhM+9RkocAZ1bVf51am1oMc5K0pZnQyYGJnMTR4pDkU3QT2VxE18USgKr622m1aRySnAicA6wAfhP4U+ABVfVHU23YCNLPHtt7CN1Vgy/RnbBpflbZxTihBkCSP6C74rhTVf1SPzb172uBzZa4uZK8EPhUVX0vyeuAfYE3DTsB2ELSj7V/4sA4x+3oxm8+9v4fOTmLap25JCe0Um+ST06gzr+aQJ0tvaatvE+tvPeTqLOJ96ivdxJtPWbYx1bVz6vq3Kr6SP9z7qhXJavqtqo6rar+tv85bVJBLsnzJlGvJmqPqjqyqv564G+k6SDX+xPg8cCPgQ8AdwCtX726EFjT//4c3Wy6z+3L1kyxXePyCzNBDqC//aAptmdcjgWeAXwXoKquBh421RaNx1/0Qe5X6GY3PpFu0pDF4BTg/CR/1X9POg94/zQbtKjCHPAPG99lwdT7BxOo88IJ1NnSa9rK+9TKe9/K39MkXk+YTFtHmVBowxVO5uTAJE7iPG0CdWqyvpzk/5l2IyZgn/5nG+AX6LoFXzDVFo2oqvaqqv9Ed1zvAi7pf/4/uuDauh8kuXu6/iTLaH9CDYAfV9VPZu4k2Yb7WQuxITMnGg8D/rGqzqSbcKh5VfVmuolqbut/XlpV/3uabbKbpSRNUH9mcj/gqzXEun2bUP9uVXXTmOt8alUNFeYzocXdNf+SXEE3LvMbdFexZtYsa3psWd9N6v8FvsrABEJVNXuCoeYkOYPuKs+pfdFvA9tX1RHTa9XokjyNbmmWb/VFuwEvGvb/qYUiyV/TdWU+iu6K8R8DV1TVa6fZrlH1JxlvpBuDvS9d8D5/mKV5tHHNXZlL8sSB2w9I8rokq5K8pR84Omy92ydZmeSqJOuTfCfJlX3ZDkPWecis+k9McmmSDyTZdcg6t0nyh0k+1dd1aZJ/TfJH/WDTYeoc+7FPqt5JvP8Tep9aee8nUWcT79FAXeP+Gz1/4PYf0E1c8hDguCRjnx583EGur3PYIPdqui9coZvd6/z+9gcnceyauEOBvYGD6GZse17/u3XrquoTVfWNqrpu5mfajRqTJ1TV71fV5/qfPwCeMO1GjcFlwN/TnVRYR9dz4vKptmg8VtAdz2V0yy2cBbxuqi0ajyOAs4GD++VvdgL+11RbtIg1d2UuyUVVtW9/+2+BnYH30S3kt3NVHTVkvWcDnwVOrqqb+7KH002pfWBVHTRiW98L3Az8I/AbwK9W1QuGqPODdGdxTubeiwcvpxtAu9lTv07i2CdV7yTe/wm9T62895Oos4n3qK9rEn+jgzPkXQA8t6rWpZuN7dyq2uxua0kOmZmxMsn2wNvoui1+FfifVXXLEHVuD7yG7n15GF3XnlvpJk5YWbPWn9vEOieyuLs0TkkOBF5MNwnK3TOsVtVHp9aoMUnyz8DfVdW5/f39gWOH/W60UGTDVxx3qKoXTq9V2pgkD6PrygxAVX1zis1ZtFoMc4NflC4BnlZVP00S4CvDdv9I8rW5ZqK5v20bqXPwC+glNbDy/ez7m1Hnf1TVYzZ320bqHPuxT6reSbz/E3qfWnnvJ1FnE+9R/9hJ/I1+BXgmXc+Hs6tq2cC2u1+bzaxzEicHJhFkr6I7E3vdrPJH0c08N7XZvqQZfeB5HN2VnZlullVVvze9Vo0myWV0J2RmpoP/Zn//UcBVVbXPFJs3siRXzD6GDZW1IskZVXXEwPt2L4ugK/Pzgb+lWx/1Vrplb66qqsUwfnPBaXHR8O2T/De6L0rbzZwBrqpKMkoyvS7Jq+i+2NwC0Hff+l3uWYNpcz0syZ/RdTN6aJLUPel52C6u69NN+fqRqvp5386tgBfSDcQcxiSOfVL1bp/kN+he03G9/5N4n1p57ydRZyvvEUzob5Ru8pgAlX5MW7qptMcxAcqygfD69nTrxQ1jaVW9dbCgD3VvTTLsl9pXMJnF3aVxetoiPLGw2GeLvSjJAbOuOLY8S+fM7KmL9X17I3AA8JmqekqSXwP++5TbtGi1GOa+CDy/v31ukl2r6pb+jPK3R6j3RXR9l7/Qf5kr4Ba6xTeHHTj8j9yzYvzJwC7Aur6tlwxZ55HAW4F3J7mN7svh9nRTER85ZJ2TOPZJ1fsF7hmzMa73fxLv0yTf+3club0v24HR3vtJ1PkFug+osLDfI5jA32hVLZ1j08+B/zZMnUwmzI49yFbVp5I8hgks7i6N0ZeT7FNVV0y7IeMy+2r4YjHriuOXk9zriuM02zaKumes8z5V9a+D25L8Ed34wJb9tKq+k2SrJFtV1eeSvGPajVqsmutmuSFJThl3f/Ak/4XuC8llNaYZ6DLmWe2S7NzfPL6qhj7j0Z/huqqq7kg3QcUKutmHLgfeUlV3DFnvnwIfq6pRru5tqN79gZ9X1QVJ9gEOoWv/WSPUN/v4nwJcwZDHP+FjL+DrdN2Enk4389Wwx74t3diRb9EtDHwI3Zo3lwMnzB77tIl1bkcXBG+sqs8k+W3gPwNXDltnX+/j6ALCeTWw3lAGxpONUO8edOPZxlbvOCU5blbRu6sbh/dw4K+H+f8v3YLhK+hmnpxZ12gmyK4sFw7XIpXkSuCXWGSzdC5GfRftObUeYpN8GXhdVX22v/8q4Neq6tDptmw0ST5DNx57Jd24+Vvprog3vcj7QtVcmEuyagPFz6Ib+0FVPX8D2zel3vOrar/+9u/TLeT4L3SzeH2iqlaOoc6XAR8bsc6xH3+Sy4EnVdVd6daX+gHwEbqFHp9UVb+xuXX29d7R1/V1uoVZP1RVo1w9nflSeyjdVeXVdOH483TT355d3fofm1vn7OO/E/gwIxz/rGP/IN2xr9vcembVOYljP7Wv74F0C+c+mO5v9EC6/x82uwvfQJ0Poptc5ReBj/Z1UlW/O0Sdf0L37+dK4MnAy6vq4/22u8eTDVHvn9L9Wx9rvfMpyUur6n0LvU5poZgrILQeDNSeJLsAn6Sb6fEQupO0L66BtedalOS1dAtp30zXvXJ74NSq+s4027VYtRjmLqa7avBeuisUofuyfCRAVX1h2Hpr/DPQTaLOi+iuGI3t+JNcWVW/PFP/4BfYjDa5xMXAU4Fn03Vnez7dWKIPAh+tqu8NUedldF+6t6P7T2KPqvpukgfSXbEZZnKNsR9/Q8d+aVU9Md1CpTcCj6iqnyUjTVYyiTovA55eVd9PspQubP9TVR2fIScVmWS98ynJN6vqkQu9TknSfaWb8fEzdN8Rfm+gC32z+pPPRwDrgdPpTmhv9qzL2jQtjpl7Kt3A0dcC/6uqLknyw2FD3ICt+m5HW9GF3HUAVfWDJHctoDqXMf7j/+rAmfivJFlWVWv6sS9DdYnrVXWTanwa+HS6dcsOpevW9zfAkiHqvKsfe3Nnkq9X1Xf7J/phkp9v5LFzmcTxt3LsW/VdLR9MdyVte7r/fLejG6OwYOqc6QJZVdcmeSbw4f4M+yiTikyq3rFKculcm4Bh1y0ce52SpI1L8j3uOSFfwLbAfwJ+MwlV9dBptm9UVfV64PXp1p19Ed249Buq6tlTbtqi1FyY678gvz3Jh/rftzCe45jEDHRjr3NCx//7wPFJXkc3QcW/J7mebhKE3x+h3nsdYz9WahWwKsMv8P6TJA+qqjvpgn33RN2aWcMGmkkcfyvHfiLdIPKt6U4QfCjJNXSzUJ22gOq8JcmTq+oSgP5K2vOAk4DNvsI9D/WO267Awdx3htEAX15AdUqSNqKqZib0IslOwN4MrMe2iNxK15PoO9wzNltj1lw3y9mSHAY8o6r+fEL1PwjYtaq+sRDrHOfxJ3kosBddOLxh1EviSR5TVf8xartm1bldVf14A+W7ALtV1WUj1D2242/p2JM8AqCqvpVkB7quod+sqvNHaOtY60yyB92VyZs3sO0ZVfX/L6R6xy3JicD7qupLG9j2gar67YVQpyRp06WbT+HldJNwXUJ30vPLVXXgNNs1qiR/TNfNcgnwIeCMWkSzxy40zYc5SZIkqTX9uO2n0c2j8OR0syu/pYaceG6hSPK/gdNner1osprrZilJkiQtAj+qqh8lmel9c1WS5he0r6rXTLsNWxLDnCRJkjT/buiHIvwLsDrJbYBLZGiz2M1SkiRJmqIkv0o3cd6nWl9nTvPLMCdJkiRJDdpq2g2QJEmSJG0+w5wkSZIkNcgwJ0mSJEkNMsxJkiRJUoMMc5IkSZLUoP8LGnuLzRQRO0IAAAAASUVORK5CYII=",
- "text/plain": [
- "<Figure size 1080x1080 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig =plt.figure(figsize=(15,15))\n",
- "ax1 = fig.add_subplot(221)\n",
- "metadata['age'].value_counts().plot(kind='bar', ax=ax1)\n",
- "ax1.set_title('Age')\n",
- "\n",
- "ax2=fig.add_subplot(222)\n",
- "metadata['dx'].value_counts().plot(kind='bar', ax=ax2)\n",
- "ax2.set_title('Diagnosis')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8858805d",
- "metadata": {},
- "source": [
- "Correlation"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fbc246dd",
- "metadata": {},
- "source": [
- "Cross Tabulation of Age and Dx (Skin Lesion)\n",
- "nv = Melanocytic nevi\n",
- "mel = Melanoma\n",
- "bkl = Benign keratosis-like lesions\n",
- "bcc = Basal cell carcinoma\n",
- "akiec = Actinic keratosis\n",
- "vas = Vascular lesions\n",
- "df = Dermatofibroma"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "a0602660",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dx akiec bcc bkl df mel nv vasc\n",
- "age \n",
- "0.0 0 0 5 0 0 30 4\n",
- "5.0 0 0 1 0 1 81 3\n",
- "10.0 0 0 0 0 0 39 2\n",
- "15.0 0 0 0 0 0 73 4\n",
- "20.0 0 1 0 0 6 158 4\n",
- "25.0 0 3 0 2 16 221 5\n",
- "30.0 1 4 6 4 34 410 5\n",
- "35.0 0 5 24 12 36 668 8\n",
- "40.0 9 23 46 9 49 846 3\n",
- "45.0 10 26 59 14 74 1100 16\n",
- "50.0 19 27 87 18 96 928 12\n",
- "55.0 27 25 95 13 142 686 21\n",
- "60.0 58 35 131 9 106 454 10\n",
- "65.0 38 79 108 18 133 351 4\n",
- "70.0 56 85 183 4 166 248 14\n",
- "75.0 47 76 153 9 91 231 11\n",
- "80.0 37 73 98 3 85 97 11\n",
- "85.0 25 52 93 0 76 39 5\n"
- ]
- }
- ],
- "source": [
- "ct = pd.crosstab(index=metadata['age'], columns=metadata['dx'])\n",
- "print(ct)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "0a82f299",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dx akiec bcc bkl df mel nv vasc\n",
- "sex \n",
- "female 106 197 463 52 424 3237 73\n",
- "male 221 317 626 63 689 3421 69\n",
- "unknown 0 0 10 0 0 47 0\n"
- ]
- }
- ],
- "source": [
- "ct2 = pd.crosstab(index=metadata['sex'], columns=metadata['dx'])\n",
- "print(ct2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "ad813357",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PValue = 2.4464388098587195e-17\n"
- ]
- }
- ],
- "source": [
- "from scipy.stats import chi2_contingency\n",
- "#Sex & Localization\n",
- "chi2= chi2_contingency(ct2)\n",
- "print(\"PValue = \" , chi2[1])\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "20474e6b",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PValue = 0.0\n"
- ]
- }
- ],
- "source": [
- "#Age & DX\n",
- "chi2_2= chi2_contingency(ct)\n",
- "print(\"PValue = \" , chi2_2[1])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "864a5b70",
- "metadata": {},
- "source": [
- "#### Step 2: Image EDA\n",
- "We will perform the following analysis on the metadata:\n",
- "- Average image of each label.\n",
- "- Contrast between the average images.\n",
- "- Principal component analysis (PCA) on each label."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b80a2f2b",
- "metadata": {},
- "source": [
- "Average Image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "971b33b7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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MZIqZudaqncpscEYUuGppM66svReMB8KSy3hBsjavtrqVMet1ROfLwnsZ7KpO+4m9ykaKzSj73hjZpGWWf7YevSXy4qO8I2XuZZErYHzintWOQClx5ey7ApYmjKBKG/RvnSbrSHbD0lxGVtkRRfDoVcWFF/HrbBxGJq9+Rcmqqze5LVebh3RjN7v0s/lXbV4iVOhENGiZwvYDr+tu5W0DFOXTyuT1DTMB+rDMWIyMv66rVtb+JC4qg508JTrB8OEob/+/fl5plStc10rj5dcKGFmpiA8Dd7a9/WFCK1/WR8xE92RZbRnBDP/u+08rt473MMSJey7sxTPK3necZ4FmXEgMpWCtse7cSI6Gx/u0Imv01CPzcGS8ObK8Vh9UPAhRGuutZ89CW/VlMHwBSIePzGJGYTMeV9m4sfxXw1Iw1sJbdYzqbil2r8x9moxSRJbXq69+Zvt3lDdbsLhyBOq7E5mQ1slZumzJY+lE1lkjFlk/W9TBk8XKb9B80Uvf4nq60afJNncR1ZjdOFuIrPeIklv0wsKUi62HRTG0lY46ju3QzGqOxFvKO0odvE2fp4TMwIrIXXJZw9HKY/cfLCpKaR03V46gpyyxFsS62mZvrVlYsVRFCtx3KLPRYzwUDVoJ9SauT6dleEtrs84RzbDCMm49A8b69uX3llYbv8p4U1cxLe7a/lUONLznFcgoRGShLevr5fdoBls3y6o2WKdWHpVqciKaYZXtbexG9zRVT0Impyl2q0925AwM0Alv51vthD04WS/berbiIqX0lN2K88IaPP7ax/UbuZbW8rf2/+s8DE7NlRustrD5Iky52KqFRWmjjvVQ4cQVrtz/9hS34hXJPBGepdYutp4Caavq5dF1Yzbfe3Boq4xVKB927JGWzTPa8MwDoflxpMCMIrc41hXWx3l0gZVh5dHys2evPVoWg6aw2UbOoqzseFPHzh73nflgYGVnXZGpnzMF9srxFNxLn1n3qjX26IJlhfvyLK7cx+nnapsq8RZ0P7ButAhLXGx9h8x4JqrLlmXtorRAbF29QcwmR+VtBf3CZLYZa3GaLkQ7f10nPQH650ihqxy2usnz0vSbO0a5yxrnWeaVbhpPqbw0mfJl/Fb/7i2DVU4vL7PYOq/Oo+V7E0a3ocLJs3hWVjTR9kIrM9Iv+vUk73fFClcs7UgnRRYzcqMxCqzzsPKs+nl+4xZnWT9NMbSP2Mvf6qpdcZEVzuq+ApZ7TpfVkPFp6kXR6reI2fAqfYgQKX1kLS0rbaX1FD0q21vyPf4N+N/u9fKw3DdzxTFKyno2PHg0YfQF0oZlnDj7cPIKMEuhp1xR3shLYXHfzOJm9fJgeRc83tzXifEr6/Se7MiweFw8a6POk3Hnpk8ZdWoYPuxgZmWPlVa3l2n9rtADSzEZLs1Y4mzSeZuszE+sn61Nn5bbl6cVuU+bocKpI49MNiEqoG+x9fx35d2IrPMyBfEUOaMHWjEtRc8OOqzJwMJSUD2okUfCowyeXJ1Wt0XXYbXB2RPLbrHNoDorKwocWdZogmT3KbzJEsm1lnUvvbeR0y5FyypHPuWqxWXSWpu0EQvrbe48utowZVIrs5XtjAyVzomUzrOsWoH7f32YlqXTefXR+S03nVenRo2sianbmPH3yn1pL5xJO2rRK/mmLfGsclYPN7K46CQuO2bOrK6W760EGSzL5bnNtAVt6SwK4lEBbZG1LC9d1A/WijDTB9Gd40wmfRUz8xmvAKOkTFzGb700AO6wdjrcK7NivaKlV7varHSW8un4KF1fN1aRmbaN0Ie+7jdv3gxpWCSfphNNSPQVeNZzUYVWGGvZ7pfbPk/U2VrJvVkfcWxvmY0U25MR1ceiIlY7PdoQ0QVvU1pRzMgtx2DmQztDf7NjBr1VX8GBmU5iDjssmRY/9p495bTi+nIyaqNXBh0Xtauq8BZmLGwVo/o1/bbzbAVmUBmkSIE8C145WmYHW6frl0prCc/4snd40cvOqAVw99G0VecRV+gpMP0ttioyrpXl1c/MneBIuStva7CWLPMZM28uWz5jz19c4bU6LkpzCqw48Jg+tRjhMtkSHnFKC55V9Hy47Z9FFdrv7L6xhd61FdWxd61ZbjrvN+u79srOJtfM/YURMBOFubdz1sMOq9MrO+PoIg7rJ/UUxcurFb6i7JGV7aE3ylZancY6EGl5K9bYk6PrvwJeX1mHHsMbu3MePXqbmQbGWW8t/4xFi+RFVCTiy1m8Vz/POlsHH4wP2zut9FbHSvgIVshZ/oGIWcXPGmUt9RqVo2DWqmYKrNN4tKgP9xS7tZFxGepyozwRjevRt212PLUnKnqZYvR1t9KfOxgpZMUmIVPAzAprumDl86yqlhvRjX55ZuCdckVeBX040o9J39ceZWht1ZvEq3ThR4P+60l9Iyund7O+4GjJt9JFN8+yI8zMgkUXhyJr1/I2ZfQuqHtKbd1Os8qe9TJE/PkyY8nGbrahjPXyFIlRqIzvWidjWble2X3aLJw9ZtbWU8vQ4dYNOU+eBqvIlk+akdXaqvtixtU2rMSRZZ4Fq9BR+swyMvzQUnIrrcXTtfJEaLShITvY6MvwDjIy33GvyH34irFkV88Klh0735V50eX4qhX28mUuN21l9fVGLdvz+3pWO4uPJlT/L7pKmV1Q6uOi+yAe2L6O2tDgTYomO6Oh7KQqve28BxirGuXzuHCkwJ5i9fHswQLjV2ba5w229+VLCx431nF6mc9+R2XtgSq1SE1pZYnZ070Wbaq8k7eojGgDyD5nngrLOvXptUwrr+Uz1j7iyBpnfWGtNCLjf5slaqMV38qL4H2eoGHJVzEjsBWtcNdKHms5zW6mebRk1PqydfUu8DD3gi3ryYZZ8Dwoe6Bvs/cp1yWcuPdHzljcCukf4XJRvGXZdF5Wgb38ujzPwlpWykqv66WtcVR+n5dBJO+UqOrX0mm2V+Mjt1rmF7YQKWmUPrua6S3vkfxIoft4Lc8Du5HLVqssTxWVNlRx8nfsLId+xQL0aSOe3KfXVtgqP7qLkd03aPL75b/BGzTr4MOiErq/enpm0QyPPljuNAs6znPVVbC3ZZ+2xHtXcGQpXGExomevvApV0m407c6z5Fu3u9i29Ognximog6f0K+gpsMMFIAtsR+nGzHgQvHJ1+iqfjDirdWmH+dfLiiZtREm8sIiqRGD7gKkDUwaw0+tJ4Y6wsGvdw9+c8c3I3aR/Z5s1L7z9jg5ZGjLfp77YA8C83KOX9+zYWaNCJSrUwUqX5WVPM7M0Sy3xSEMqGF3+Iud/tFnLTq1Yi5ZZYa/8Sjk6ffWkzisvWiH05GbGZw+X3bBEhqvOKDC7s9fPlYGLBiDazPVprZNCRkkzTm3la7/1yqHdb15bGXoWhXnYk1czsk/CiTU8vhZVuKLIlgXN/KqRPE9BLOWxlJVRbKseq9xRenJm9GsEzHH4XrgSfmKN6uUUK62ndE2+N9FaPHPkXCk/m2DVCcBSCmZCR5Six7nuHVN3J1hUG6HvZUTctVo+w8+8m1+ZglvwlNCywiMKHEFb7WjjVpE7AmZfxPLi/ibb9FcxV3+P+JSwOCNTB8uqWpdxPOX0ymEUWi//K1CZCKcYo5nPVt0li02YOaxHwS6RVlqtDOwSOgJ2gL16WUodWWAvvdUHXtm6nL4t1T5iKcU5cLKNHWsBT1WeNRiZvzdSTE8RvbKt+yCsLE0fZvuV4cWzcqODrFmUv4rpYQWpZ7ic9W6WF5fBogo6XFv3SMG0vP5fHxZZNUv+6IZKK7uXpio7GoMmM+P7K7+kuswSj8zYaGlk5TMWfka29Zu1nj2i14o8i2XRBSuv9XvVqmfJGeGze14aGlbiymbPq5DnmZixHhb/02CuaHppGYvsHYAwiJbgqPxZI9KwYnN3StoILNjYRVhpEVa9hctaTkaORyN0mFW+VYfo9yiHzJb1qPxKGd5kHXGhVj/SM/S3nbNKsKjkzTY7I4PcOj2znNEgseVEqLylMWLVZ6xr9fqnzhttbrV8Hcfi5F+K98B0ULahaGErrD/bVs/iWumi+D6cWTFmVzlvI+vVycubYWQS6M92penp2jhY/bGNUegv3jA7c6ZOrIUExj9kmNUhQqWNrOJ7+4DZse7ze3uqkZX+JG92ZB/CYI9JZ04OM+tmDXD0bh8L63UsCwyXHrkwX03rrRjsBOjH2suv755ksjKUtWKEF1XiRq0nm08vo95bC5m7KuqHyhLqbYQqE2fV6lOBVY4eT29TFxkjT/GXvLK/Ant7KmaW7gyZ7OwELVuFVl8WP7Wbi0H1pWI231nuE+8JZpMVhY+W5X1h0sOeX1VaRS2q6SsuxJWTtiTplLO7sqE6NSzubLmxIk53ONz9arwlewYjlm/WhZr5hUeQ1WGpd4LdMERxszN0b8tVyRNthva6FRiBkT06ifoJe2ojQ2lM74yvztQ2kNUNXi97T0qwh6we53pth/WXe8v/ua5cehvdCEsuxTOOfLZCFcy+CZLJYuKjwe5vaM0ss+d67SeD1/aZVcYyktmEpC3xLDLlYhrI+hZXWzhPJvOuX++mY/2eWt4ILz3Fkr5qc5b1yTQnZnf7ESqDx7wr1uphLT2V+o3w+Sq10W1f6Q2IwB4ujfhyrfiRelbftXPllEt2ComwauAyN9bshPP8u8wkZOOzPUVvfWdoRDSpmElZXX1H6sp4ZBjDRHPi2ddLqidXPVrZ1Q+2WB2r3yiILptkjvelvs6irDaxvPqv8kNX+4RFdKe7cgcFWPTKvvaNMun2RGVg2HfVZjdnTF3637ostk36M6zVepzD9TeLqXfsqjMmSssoyKrXvBm/NGsZR6wxS1FaWl0WYx2jOwejClk97o9ksP3GyD7rnwCrdqZ+p2xVubN8d7YfPOUcqUufrrLR1ekqx+ijk4K9CJTKGSp9oFDW8oxww2q+aAPnybfCKoNXyVN1xY3g3J/mBdYdqAz5ia2bWxms5e1cdyBYC5X9KQEd1tME66CDObX05I3+BXqrHVGZESI6MTuWmuJV2lr2E89ihbwRS+jJsH5H3hCWr7PttP4qFevjZXCqjfastfY22wyG6MTKzYLGyIdCMguXbaQ8TtqHM0pvlevh4uLCtJAVunGKvzG3N1ZMJMrFNruDb3KYsMomJyprdImsbK4spWf6pHJSldUrWpUyN91lcJutqNOVmsrlt2AnLFVmXb3/W7mMQntKpsOZdnj3mNk6zHhoKoo3shdJ05dST2AF+W+IaMGIx6DPqy2WtTJ41sOydr1CW4odycrqzIKx6A0jm/ZzY7kSM/x0RF5lUJnDjMrA9jIrS3wEVg5D5ap1inzAngVf9eICO4kqmFJi6ysunsUdGXBPgTP5ld1+j8iSV5dbXWa2uWQtvVcXS+ErK9MoP96DV+/inah+4G+EX824pFiwu3qtcBGl8Lislmf9thQ9qlOWJpOTbfQaGAq1J5ZyYs+q7nFIMUozmLwrXFHZplIr5qjSZV6Gvi56okWbwz5vNKky71B1c61hce7ZCXKW707sfbG+pY/+99JXwkcog/Wvj4vK9SzpDM9c3S8ZLM49axQvrYtt1F/M8lptuaxwVkksOZ6yWnXxlJOhThVLn1nRUx6enOXPHawAy5VHO3N0E8Mqq54grPWsKHBfliXLmqTMhK/E7cl/Gau727Hz7N9+iGB1GtuQmWWwYuk87qnzZhY2ow+9L5m15LqerMJak8aK18aFNUYrEekDpcR7v9xYfROa3T1HCluhCLN8OVP6rO5WG1oajz7NuAtH0zZcuo+ntArp7/9qVDsmU8AMEb/b61Vyz1q2/yubMStuRKYnW4d7J4UrFF2XtcISs98yAQY48WgF2dkZ8eLKshq5ofpwL0zLs5Z2b0Xw/lntYJTMsrieFY6sMzt2nmXPcK5ruyVOzN5/GJ3FGsyRcRTOcFUvLDvWzXiiN+GyyRDFR3WvWGz9POoJYmhRhhWfKrsnK2DF0rBChoWLi4s7jr6j42jvVPFwONwlx0pv5dX5V1g/baU9ax95L0bKt9KPtIf9CM1IvIdQiVdg1CrPejs8pW0K11946RVRX3RhPp7d8ntoMpjXhCLF8WhJxLWtcjNKpLHqzeR+XHTf6vgKhpV4lZXOoBvbGmopn7airfO9P8itLaqO86x0S9/XRYdb9dAydJhWRs+j4snT4ZlVjZTTKktD33xjVrGGlR/f2eVt58t2t6L99jZJLT66f+DJZ3m4J88rw1NIa8PKWtFsL7FqL7MamcIPWeLe2ty6dWvIpVVZeqIwJr21dPVpovo05bZohTeA0SrFKL33bl90COLl8cqIEFlha+Vh5DKGbdT4LXvbORrQUZkNmY+zcrOqz+MtsZYiWJaRscY6j5c/4qh6g+fVzcpj1bE3QF6dT3mPoof+66rLvgDEUIWVr+KPpo0GzBpQ5lUhrWD6FSNLMXX5lrJaab3DDoZCRJTAi4uuZVqwJlDDyPjPeDJ60HTicDiUqIPe4ERUoM/joXdj6ZciI6+BBV0vXeeePnjLp7fhs/JYZXsusvZ/ZpmjyRlNZqb8EazixhcXF7h582Ypz25+Ymtg+zjGp5spfh8f8d4Gy42WybMmTS9Pw/MX63pY8rx0lnLr/P3/2UGNJTdDNCmrVpjx7e9iiVdilnpoZbfiLReYp4zW5AFsa5tNRk+xdVy0jGsK4dU3okOR5a4iU3KrLaMbucjIeDjbpfiIX3nhzDF0tKRa6TPfquV28357nLjxaCte1zVSYE8Zvb6MOHfUT6fe1DE0M0ozZYkjR38l/x6nc95hhEU7rAOTVr8WD9z9QW62rZ6FtNJUFFhPCs+DkZUZKfAKrtuPT/SF+FEsmXKjr5pUOFmD9g7ouGr52Qapav10fGShIyW06mGlidqUrUL69x7+fq8e2cWfCig/Meuvs/KNwOKu2YBES7SGda1Rh3vyLBebp6hWfb00lfu+ngWNaJUX30P7jRnjwqRp4zly1MzkCemE51JjT2kq6GlBk2udzWfeCGvp17K9UzzvLkaL6+vV0kUbOQ/Vi0CeIra4TB4zQXQali6NeLA84zb6961L6wc7M3SeHlpBVyAbfB3nWd4o3MtvlaWtdX9I4h0de1Y6UsRegTNqlq1MFUQrSmVcWUqRrerTnHiEZkTIljx21jPcMXr2LgNlyu0pc0Y5IrmZonp17PNGqBimiFM3zFwGG+HKYQub5R2ZXSOwBiFTWiZPZKkjt5N32GBZeEuZmfTeRGllR5u9bDMa0Yy+jZ6VX4k9v7JJW2LLpDOukz2RWShmEHU6rQyR28qzqCxV0Ol1G7zVJLPEVhlRfCSbjatC69KM663sJ27W+caNG3eErZ65PUS4+xJ9XPtfHx97eaPrlv1mkW2ndzoYtbGvixWuLWhEd5gJUOHCs+ObHfkDvGJrnPXNDksGc2+iD/fuOPQymHsQWpYF7fmoeGmyNNl9h/6Z9UZEFItR4H7iZ8jSea95Wau7tepHCr307kRzOXknX+xgW42IlFqnaeVYlrePs+qkFVmf/LVJ0dob1SUC42azwr3TukjBGTphlTd6KtnA7o9mvy6VKvFKV5gn31J4C5aSemk0vKuTXlogpg+9MkfIXhLt663r5llPi59bsqw4qy6Z5R+FNxaMQWqY5sSWVYvSRAMVbTpmqYl38OHxY6tca3JE6S1EE6LJiJAp8Ay/9TaVKxFt/Gd/R8pM0YmIJvS/q+/beR07QhsshbPkZek862dRjAaWLkWWyXr2vCb6/yidJ3O1UmcK2eRn+5xGJXujtPxF0f4cfObKHsPPsgHX0ApnKRej8FZY39bsNlu0eWR5aWR5o1Utk8mkqyIzOtFx8grX7G6X4lfu3C2ltpQ02gxaFhmweW9Eo6xJ0MdF7fE2mT0qG75MmS2aN6qsI/sQDxU+vMRPzAjpB6+30Bl/rCo421mepbV+exeGrDp6HorMNee1R9eVcY1lYawCs8qc9d8oNN/NFHaYEzOKtsqPaGEkvbUp89JZAxJZeCufPh5uiG7YReGsp4Dhy7pdnqwIs5vuCFYfNT7MbuqAAp2wOLAOyzZ+2e8qoiUOuFuRLQtrWVZLeXtE9dbKXWmHVZb1e9QXXKlPRI0sCjWKFVZ9GSfec8b2YKynFd/qaKWxLGt0Cmihsgfw2sX8Zi3wCI0YtdYtPbNxs9J4YdpL4WGXNwIrO86RmSji37vVz9ZbGJ6MHt7BQhbW//PqFqX10ltt8fJUFHjGgq/czPWc2HLXRflDJfYubPSFVS91rMSIInvpLIXyXkOywjJlzyZNlj67TN/SsAo8Y3UBzi/M5u0x4nKj6IT2DfcbnCpW0w5vGWtxrUzthYgoiJcv8kKM8OeoTQ3sQcaprC/AWVXrd29RmfsS+rtsHlI6Mfo60invF3vLrxUf0YtIbu+F8DZWFZqT5fes6qwCX2Z4hyJT3onencR0iE7HnurtsSlkN299HGNFPRm67KheEaqXc6oKvGLDFq1+VVqhfcQj9LP0QUG2A/ZSyiZ7NJ/u/OxKZd8Gz3fsTVLPT+yVFbnNvOcRCrE3+j7OvrmWgaUfJRdbtoHzOmq1f9iCZzGsdK0ODZFyZbxZy+plNvQrUvWmn0dPZhVYT+4Iuu3V5V7HM86A7G+l9KA+nuJVQM+SzBUSyfWwyqpEngMgv7PQ5/FkeZ4K9i2KqH6edyPzQDByzwXGlcboCcWJe6+EjvNm8ipryx429PGVpUtz5Yjjeh6MSPYIMiVj7licYqXTyKxrdKiRyZvyTnhCNTLzX1XGStlaZrSpYbwQ/T8vnZaZWeOoXpU2MJerMtmzqMhhqYS+P9yn2eU+sbbMvdVlPRRVS+0pROYj7suL8rJeiIj3V+odQSvyLI/eE6OHWZaSRnRi2BJnu8OMA8+c6pwCFVcY8/pPr3yR1Y7ya8W1VoNzWmFWLrMxy1xrrL7QJ3Y9J6p2yOhbIAwPY7hpn64hOmHzZEdtsCx1pLARtAJXvBaVclgwyuRZ1qqMRiUqealL8dZA6w+otMqPvrLEHjasgjdBLNeZt6HtYbW7P6puaUb9xFHYnqhaSPaktl/hoxWd+VImfWLXBOov/zSsUMAZpY0s6mgeS1l7ypD6Lw1K0BR5Jc89l7IzVDKy0BnNYPgwMPGiqBXube5YrOz4GaVmXWlZfXWfNFSsrtWXjJuRDT/FPqVCDbTSR3uyBuoqZuUQw7sEzSwLe3Zo1ROy4lCAzT8atwKjE2IPsJ+60pg6draWRYs/RvIq1mwWnmXrua9HH9i6ZuWzdRzNOwJrH+Bh1NBEltTbyLHGM+XE2idsbei8vCuUb7UlyPjjCA2JZFdlZPXpwyp9XEnHnKAxyNJbBx/eQciwn9gT7lGMFjb7gbjRtHuiqvw6XeQ3tuiLJXOU4swaAn34AtjLPUsZPQs7sgkECEsc/W6FWV+rsWDRj5lNoLdxYvL25XthfTlWmTqftpAeNam0awQrlJbtz8xqM9xWGz6dN/UCMRVlzrK9mVXZFEZyMsycZFUsYIWbRlbWkhuVwW7+9tyIzfDhSD+83y1vVvbwBaBeQa2Csle196YJs94Fz0NRVWhPZlS3Cs2wJkEFHiWM0liw7j9YFtainJaRXOYnzpZanVYvvTquVXAlpcjCVlipiBow3hd2wlbqV5GbIer/U+5fIsWdtsTWZo19EzXiRF4c0xl7WvLqcp7JYelEln92dfHqWJU5Mu6RG81Kr/NGKH13IqpM5gbRcq8iMjqRLelVZTyHZ6ayUWvwDik8F5rnechohgfaEns7SC+trmzWOacAq3TaO8FYyZHyvbhZnnsOZCuupUOAf+Mt4tYaU5+x0sfSVkFe4aPfs8jCPGjr6W3YMvdZJt8K96z3rBekki5D1Zj0K7AF5oQuGtNeuTOLXD6xszZuViUarE2h3uT1G6C+nJVWqJc3o5BN1kh+K76qrHqVsGSMKKS1UfR+V8Ozsi3F1XHDShxVqLIRqxyIrFTkvZZiT5lXegyqddeTdNSyWr89Wcx5gV6pvZXbohBZ+Q3U60mVl/h0RTMaMPoWwCnAWNSIpnjpZ7jubH6NVX2dbeiifDotc8DRY/hvOzOuD09O/z+b3ssTyVkxQIzCRL5pRsYKxazkzfrF6nP9P7tp92RpF61l/HS4B8oS64roZUAXpBtoVXoU57LSo37iar69PRIW37Ti9XNFthU++rFARmdoS5wdI7OFWp1UXT4imSvAuN1GZFhpTulCG7XAjBXu45hV2jNwfX5WL0qWOPod8SCPyHtpdTyjpFaa2VM2LdtywVVdYucAq1TWsw6LXGeWobOogqfoDI2xMHSfuFXYa2xU0Srpz54rdbbAHCxEhx1e/CqsOlpmwfSzx3WjPF4Z2vJa1n2KE2e3i/oCWGscNSgK0+GVfBlW0oP++dTW2drDsPm8MGuvo8vTcVG4d2/YMnZenEbp2FmHRcuCrnAm89atW/Q9ZS8u47Iao0qWyTy3MmuwfRilG3nR11NSy9r24Vbc1LFz5onQFfAuDWkZXnzmN2Z41N6Y4dqZjD0U3pvcWR9HPNhTsGgVtdxqOk4r/rQltgRGFdNxunKZAkbxM6hSnL3A+IuZdCwia2els/KMvDbkcVymvFamVvZonCgl1mHeFbuKQkSzN+o4r15ZPU6prCyizWGmKKz8Sv7qOPRpsn63zhL0RInoRATqS/EshWB/RzMx6oho5jNhURv3UvJRuVWPQtYH3thZ/Z/VuR9XqxyLOlhlWnGWQmd1Kv+5A6uSVjyDaLOQXdVcrXSn3HydoqyKMfCWdCY+Uv5IVyKLG5VjgebEjNJavCaaYVF5WZ2i5yisxwprl+EcnolIKby0FQWOyouUto/XZVkrNlN/YPDuhDXDKkraw3tVpY/T4dHzyABG9R5VXmZ5zsrKJkDW3mp/ZPVrYRGN0L8zWpH1OzOJSid23hKvrW0Dy40jFw7zBgg7MBWFtBSqYlW9TVU0aGw9mXYw8ZHhsAxFthK3/J5x0BPAoySWYYxA/7mDrFJWfAbWSjB3jkesJVuvFjZixfoL9NU6rkofWb2qAke30fRqHBk1Ld9bkZdZ4kwxLWvbN8yzxl6Do/IYRfYmXiR/5STQWHmBnZlgbLtmFTjrR2vyeHpiyWMUGChw4r4hUYWsvBkNaYg2BJEM6zerlFanMXlYjFjg2TK9PJkFjvJGe5cWb425pwOZYYvcshr0R7b7gj1ESssovfWs62PFR4q82tpGVmgvjEzISIanwJaCRpbZG+/KnRpLpmWlIwy9nsRY4yyeuaVkNYQl+5YcXa+snRX5rNWogjEaFRn9GLIKzNw80233xt6jl1pmpU9TOuFZVcZTYRWsK+rJtWaoJct6Zn5fZlSsUCZjVX5v3DwrbMmzlL5/9sY8U2K5SoN7jWtYmPoC0DWucRlwrcTXuPK4VuJrXHlcK/E1rjyulfgaVx7XSnyNK4/fA52heV/Lfro9AAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "array([[121.11101 , 122.14468 , 122.84349 , ..., 119.93995 , 118.71974 ,\n",
- " 117.54959 ],\n",
- " [122.58053 , 123.72703 , 124.56142 , ..., 121.46952 , 119.99272 ,\n",
- " 119.01729 ],\n",
- " [122.78344 , 124.01911 , 124.747955, ..., 121.76524 , 120.1738 ,\n",
- " 119.262054],\n",
- " ...,\n",
- " [121.69427 , 122.42311 , 123.208374, ..., 116.711555, 115.545044,\n",
- " 114.91993 ],\n",
- " [121.50591 , 122.17652 , 123.054596, ..., 116.464966, 115.33849 ,\n",
- " 114.60964 ],\n",
- " [120.98453 , 121.68972 , 122.56961 , ..., 115.86988 , 114.70428 ,\n",
- " 113.95268 ]], dtype=float32)"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "EDA.find_mean_img(bkl_images, \"Vasc Images\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3acaca8b",
- "metadata": {},
- "source": [
- "Contrast Between Images"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "39497a39",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "id": "e327218b",
- "metadata": {},
- "source": [
- "Principal Component Analysis. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "ee96ea64",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of PC: 6\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 576x576 with 6 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "EDA.plot_pca(EDA.eigenimages(bkl_images, \"bkl images\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3157d03a",
- "metadata": {},
- "source": [
- "# 4. Data Processing for Model Ingestion"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "64adf033",
- "metadata": {},
- "source": [
- "# 5. Model Creation"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c3114115",
- "metadata": {},
- "source": [
- "# 6. Model Scoring & Evaluation"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "38d22b6e",
- "metadata": {},
- "source": [
- "# 7. Interpretation of Results"
- ]
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "ee39f17eb82bb6ae04362652a1189337f31e2d08025196c7b18744ffeacb3697"
- },
- "kernelspec": {
- "display_name": "R",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--
GitLab