This project aims to use a convolutional neural network (CNN) to classify 7 classes of skin lesions.

Deep Neural network using CNN pre-trained model to visually diagnose between 3 types of skin lesions

This repository contains an implementation of a DCGAN and a SNGAN for image generation. More precisely, it is dedicated to artificial image synthesis in the context of medical imaging data.

This repo includes classifier trained to distinct 7 type of skin lesions

Official code for our paper - "Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data".

Melanoma Skin Cancer Classification using Pytorch and Web App using Streamlit

Skin lesion feature extraction and classification.

Skin Cancer detection with help of CCN

Improving generalization via style transfer-based data augmentation: Novel regularization method

Classification of skin lesions (among 7 classes) using the file https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T and using the pytorch resnet model. The success rate for the specific test file (unseen data) that comes with the download file is 81.13%.