Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification on HAM10000 dataset largescale data.

[MICCAI 2023] ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification

Official implementation of MICCAI2024 paper "Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis"

AI-based localization and classification of skin disease with erythema

Dermatrix is an AI-powered tool designed to help identify and provide information about common skin conditions. The application uses deep learning technology to analyze images of skin and compare them against a database of known skin diseases.

Data quality analysis of DermaMNIST (MedMNIST), HAM10000, and Fitzpatrick17k datasets

Predicting type of Skin Disease using CNN's . Deployed using Flask

Skin Lesion Classification On Imbalanced Data Using Deep Learning With Soft Attention

MERN Stack Web Application "EpiDetect" which uses a fine-tuned ResNet50 model for skin disease detection.

A small Android application that determines a skin disease by a photo

A Hybrid Deep Learning Model using ViT and EfficientNetB0 for skin lesion classification on the HAM10000 dataset. It leverages data augmentation and early stopping to improve accuracy and reduce overfitting.

This repo contains the notebooks regarding our deep learning based image recognition projects with my students in Spelman College

Skin Disease Detection Web App

disease detection model using ML model and transfer learning

In this project i've built a CNN to classify skin diseases from images.

An Effective Classification of Skin Infection using Deep Learning Techniques

This repository provides a deep learning-based approach to diagnose Melasma skin disease. By leveraging the power of deep neural networks, specifically VGGNet16, ResNet50, and AlexNet, this project aims to accurately classify Melasma images.

Deep learning–powered platform for skin disease image classification and symptom-based disease prediction using a conversational LLM chatbot. Combines computer vision (MobileNetV2, EfficientNet) with natural language processing for interactive, user-friendly medical guidance.

skin disease classification using YOLOv8