Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors.

A binary classification using Convolution Neural Network (CNN, or ConvNet) model.

Unlock the potential of AI in healthcare with our cutting-edge deep learning model for lung cancer detection and classification. This repository leverages a robust dataset of over 14,000 images to accurately identify and classify lung tissue into benign, malignant, and normal categories.

This project utilizes VGG19, Xception, and a custom CNN to classify retinal diseases from OCT images. The custom CNN achieved 95.47% accuracy, demonstrating AI's potential in improving diagnostic accuracy for ophthalmic disorders. Additionally, a Flask-based web app enables users to upload images for real-time predictions.

This project utilizes the original paper of Neural Style Transfer "A Neural Algorithm of Artistic Style" by Leon Gatys et al. We used the VGG-19 model and made changes in the implementation which have resulted in better results. Streamlit was used to deploy this app.

A simple, concise Tensorflow 2 implementation of neural style transfer

This project uses PyTorch to classify bone fractures. As well as fine-tuning some famous CNN architectures (like VGG 19, MobileNetV3, RegNet,...), we designed our own architecture. Additionally, we used Transformer architectures (such as Vision Transformer and Swin Transformer). This dataset is Bone Fracture Multi-Region X-ray, available on Kaggle.

Detection and Classification of tomato diseases for farmers in Nigeria using cutting-edge convolution neural networks (CNN) and leveraging the power of VGG19 model as a transfer model.

This study focuses on four deep-learning models, which are Inception V3, MobileNet V2, ResNet152V2, and VGG19, aiming to enhance the accuracy of tumor Classification

This repo is for Image Classification of butterfly images of 10 classes using Transfer Learning. Different Pre-trained DL models were used for Transfer Learning. Also, flask was used to create a front end.

DiNeSys is a distributed system built for deep learning network profiling on cloud-edge systems, developed with Tensorflow (CNN computational part) Apache Thrift (client/server structure)

Specific image manipulations and inference with openCV, VGG19, ResNet50 with a simple UI.

Brain Tumor Detection Project with HaarCascade, Convolution Neural Network and OpenCV

This project about transfer learning with cnn

A comprehensive collection of PyTorch implementations for the VGG (Visual Geometry Group) models

A comparative analysis of deep learning algorithms for multi label image classification using microscopic images.

This project consists of a web application built with React for the frontend and Flask for the backend. The application allows users to perform neural style transfer on images, where the style of one image is applied to the content of another image to generate visually appealing results.

Transforming agriculture with AI: Explore our GitHub for advanced plant disease detection. Utilizing top CNN models, we empower farmers with early diagnosis tools. Access notebooks, datasets, and a user-friendly web app. Join us in revolutionizing farming for a sustainable future

Click the link below to checkout the website