This repository contains Final project of CSE428 Brac University

The Ocular Disease Detection project is an AI-powered web application designed to detect common ocular diseases from digital images. Built with PyTorch and Streamlit, the application uses a custom-trained Convolutional Neural Network (CNN) to classify images into six distinct categories: AMD, Cataract, Glaucoma, Myopia, Normal and non eye images

This project is a deep learning-based image classification system that uses EfficientNet for accurate identification of fruits and vegetables from uploaded images. Built with TensorFlow and deployed using Streamlit, it provides a user-friendly interface, confidence scores, and visualization of predictions.

Refactory Final

This project classifies ECG Signal as AF(Atrial Fibrillation) or Non-AF(All other rhythms).This project consist of 2 different models. A custom cnn and a transfer learning model. These model are doing the same thing with different approaches.

a robust method of classification and recognition of coffee leaf diseases using both classical ma learning and deep learning methods, also a custom CNN. These methods were evaluated on the Arabica coffee leaf dataset known as JMuBEN.

This project uses a DCGAN to generate fake art images and a CNN and Pre-trained model to classify images as real or fake.The model is trained with 95% accuracy. The project leverages PyTorch and a GPU for efficient training.