Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification on HAM10000 dataset largescale data.
Skin Disease Detection web app predict the skin disease from a single image in less than one second.
We proposed an image processing-based method to detect skin diseases. This method takes the digital image of disease effect skin area and then uses image analysis to identify the type of disease. Our proposed approach is simple, fast, and does not require expensive equipment, it can run on any device which has internet access. Just upload the im…
This project was developed during 24hr Hackathon - Unscript 2k19. It is a service as telegram bot that takes infected skin image as input and predicts the skin disease.
[MedIA] Dermoscopic image retrieval based on rotation-invariance deep hashing
Lightweight Android Application to classify skin diseases upto 8 common skin diseases using tensorflow-lite.
Skin disease image classification using Convolutional Neural Network (CNN) and Support Vector Machine (SVM) with grayscale image preprocessing.
Clustering images of skin diseases using DINOv2 embeddings and dimensionality reduction techniques.
Skin disease classification using deep learning and ANN-based medical image analysis
Machine learning-based skin disease detection platform that classifies skin conditions from images using computer vision and provides confidence-based predictions.
Detects atopic eczema in babies. Used by 3rd-world midwifery nurses.
An explainable, uncertainty-aware skin disease image classification system for educational and research purposes. The system provides probabilistic predictions, visual explanations (Grad-CAM), and evidence-based educational information, while explicitly avoiding diagnostic or clinical use.
A comprehensive deep learning-based system for the automated classification of skin diseases, leveraging convolutional neural networks to assist healthcare professionals in early diagnosis and treatment.
AI Skin Diagnosis is a Flask-based prototype that analyzes an uploaded skin image with Azure Computer Vision and uses Azure OpenAI to generate a possible explanation and general skincare guidance.
Skin Disease Text Classification uses NLP to categorize dermatology texts for diagnosis and research. Challenges include complex terms, data scarcity, class imbalance, and privacy concerns. It aids diagnosis, clinical support, and telemedicine.
EfficientNetB0 trained on HAM10000 — 74.15% accuracy across 7 skin disease classes, Grad-CAM explainability, class-weighted loss for imbalanced medical data, Google Colab T4 GPU
AI model to classify 7 skin diseases using ResNet50 and fast.ai
AI-powered Skin Disease Detection using CNN + Groq AI
To associate your repository with the skin-disease topic, visit your repo's landing page and select "manage topics."