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 # 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.
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+5. Unzip the files into the data folder in this repository.
+6. Download required packages from requirements.txt, "pip install -r requirements.txt"
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+numpy>=1.21.5
+pandas>=1.3.5
+tensorflow>=2.8.0
+matplotlib>=3.3.2
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