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.

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.

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%.

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

DermaBliss - Personalized Dermacare System

Skin disease detection project using machine learning

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.