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.

Project for skin disease detection.

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

🔬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.

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%.

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.

DermaBliss - Personalized Dermacare System

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 project using machine learning

Skin disease Detection

Deep learning–based web application for classifying 25 skin diseases using CNN models. EfficientNet-B0 achieved 92% accuracy.

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.

AI-powered MERN stack web application that predicts skin diseases from images using a fine-tuned ResNet50 deep learning model.

Skin disease detection using deep learning

Laravel commercial web app to analyze skin diseases in real time