Source code for the paper 'Data Augmentation for Skin Lesion Analysis' — 🏆 Best Paper Award at the ISIC Skin Image Analysis Workshop @ MICCAI 2018

Implementation for MICCAI DART paper: 'Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification'

Experiments of the DAI in Healthcare project - skin lesions images use case - using Flower

Implementation for ICML 2022 paper: 'Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification'

This is a final-year project backend primarily focused on image classification for melanoma skin cancer. I have implemented a Convolutional Neural Network (CNN) AI model for this purpose.

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

This repository deals with generating 'malign' synthetic samples from 'benign' samples using CycleGAN to mitigate class imbalance and detecting Melanoma using a new balanced skin lesion image dataset.

Classification of malignt or benignt melanoma using the ISIC 2020 Challenge Dataset.

Multiclass Skin lesion localization and Detection with YOLOv7-XAI Framework with explainable AI

This repository contains experiments using different XAI methods and ISIC2020 dataset.

Ai powered web app that can analyze a picture of a skin lesion and instantly classify it into one of 7 types - including cancerous lesions like melanoma.

Melanoma classification using computer vision techniques on SIM-ISIC 2020 dataset

A deep learning pipeline for skin lesion classification using ISIC dataset with multiple deep learning cnn algorithms and advanced preprocessing including multithreaded loading, augmentation, and performance evaluation.

Melanoma Detection Tool : Website

A collection of publicly available skin lesion datasets

Melanoma Detection | Image Classification | Convolutional Neural Network

Melanoma Skin Cancer Diagnosis based on Dermoscopic Features and DNA Mutations

Deep learning pipeline for multi-type ISIC 2018 skin lesion classification with CNNs, preprocessing, augmentation, and training and inference support 🐙.

Machine learning project designed to classify skin lesions as melanoma or non-melanoma using image data. It employs both Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) for classification tasks. The project includes custom learning rate schedulers, cross-validation techniques, and utilizes the PH2Data

Skin Lesion Image Classification for Applications in Melanoma Detection. Built neural network models to learn from labelled dermoscopic image data from ISIC2018 dataset, to accurately classify skin lesions into multiple lesion categories, with a particular interest in Melanoma classification accuracy and precision.