[MICCAI 2023] DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation

FixCaps: An Improved Capsules Network for Diagnosis of Skin Cancer，DOI: 10.1109/ACCESS.2022.3181225

Official repository of ICML 2023 paper: Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

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

Multiclass skin cancer detection using explainable AI for checking the models' robustness

Cross-platform smartphone app capable of detecting skin cancer lesions using Computer Vision.

This is a project that I worked on with my colleagues in the 6th Semester of my B.tech. In this project, we present a fully automatic method for skin lesion segmentation by leveraging UNet and FCN that is trained end to-end. For Skin lesion disease classification, we use a customized convolutional neural net. Designing a novel loss function base…

Data and code for our analysis of DermaMNIST (MedMNIST), HAM10000, and Fitzpatrick17k datasets

Convolutional neural network capable of identifying skin lesions (based on the skin lesion image data set HAM10000).

Notebooks of pre trained models using the HAM10000 dataset

HAM10000 image dataset classification using Pytorch and Scikit Learn

This repository contains a deep learning model for skin cancer classification using the InceptionV3 architecture. The model was trained on the HAM10000 dataset and is designed with computational efficiency in mind. It was developed to be able to run on a CPU.

This project uses TensorFlow to implement a Convolutional Neural Network (CNN) for image classification. The goal is to classify skin lesion images into different categories. The dataset used is HAM10000, which contains skin lesion images with associated metadata. The actual accuracy of the model is 90%. 🚀🚀

Website for Skin cancer detection based on HAM10000 | Dropbox Integration

This project is designed for classifying various skin diseases using the HAM10000 dataset. It leverages a trained model, explains predictions using LIME, and provides multiple interfaces for users, including a server, a graphical user interface, a command-line interface, and an API.

This repository accompanies our research paper and includes all the essential files that support our findings on fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification

Terminal application to perform skin lesion segmentation & classification

In this project, we used a transfer learning approach to build an image classification model for the classification of skin lesion, we trained our model specifically on the ham10000 dataset available on kaggle and we were able to achieve a 93.6% accuracy

Diagnosis of Dermoscopic Images using Multi-Sizing Ensemble-Based Deep Learning Method