skin-disease-detection

A Python-based computer vision and AI system for skin disease recognition and diagnosis. Led end-to-end project pipeline, including data gathering, preprocessing, and training models. Utilized Keras, TensorFlow, OpenCV, and other libraries for image processing and CNN models, showcasing expertise in deep learning and machine learning techniques.

Skin Disease Detection and Treatment Susceptibility by AI

Digitization, Analysis & Prediction of Medical Reports using Deep Learning.

Cross-modal AI framework for dermatological disease analysis

SkinNet Analyzer: A Deep Learning-Based Skin Disease Detection System - College Final Year (4th year) Project

Project for skin disease detection.

🔬This project implements a deep learning solution for detecting and classifying various skin diseases from images. Using advanced convolutional neural networks, the system can help in early detection and classification of skin conditions,potentially assisting healthcare professionals in diagnosis.

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.

🩺 DermaScan AI - AI-Powered Skin Disease Detection System Detect 31 skin conditions instantly using Google's BiT deep learning model. Upload image or URL → Get diagnosis → View analytics → Download report

Laravel commercial web app to analyze skin diseases in real time

The classification report evaluates a model trained on a dataset consisting of 22 types of skin diseases. It includes metrics such as precision, recall, F1-score, and support for each disease, demonstrating the model's effectiveness in accurately identifying various skin conditions with an overall accuracy of 91%.

AI-powered skin disease detection and classification system using CNN, Flask, React, and TensorFlow.

DermologyAI is an open-source AI clinical assistant that analyzes skin images to classify diseases and provides preliminary insights with personalized skincare recommendations.

This project presents a deep learning-based skin disease classification system that analyzes dermoscopic images using Convolutional Neural Networks (CNNs). Built with TensorFlow and Keras, the model is trained on the HAM10000 dataset and deployed using Flask for real-time predictions.

Deep learning classifier for 9 skin disease categories using a custom CNN trained on ISIC dermoscopic images (TensorFlow/Keras)

AI-powered skin disease screening web app using YOLO, Grad-CAM explainability, React, FastAPI and SQLite, with image upload, live camera capture and prediction history.

AI-powered skin disease classification and doctor recommendation platform using Streamlit, EfficientNet transfer learning, and SQLite.

DermaBliss - Personalized Dermacare System

An AI-powered mobile app for early skin disease detection using fine-tuned CNNs (ResNet50/EfficientNetB0), Flutter frontend, and a FastAPI backend.

🩺 AI-powered skin disease detection system using Deep Learning and Computer Vision. Detects multiple skin conditions from images with an intuitive web interface built using Python, TensorFlow, OpenCV, and Streamlit.