This repository contains the implementation of a hybrid model combining Attention-Based Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) to classify the severity levels of diabetic retinopathy.

Early Detection of Diabetic Retinopathy System, an application, uses machine learning to assess diabetic retinopathy risk. Input your health data and get results within seconds: Ranging from ['Mild', 'Moderate', 'Severe', 'No_DR']

Detecting Diabetic Retinopathy using Deep learning algorithm - Convolution neural network (Resnet-152) using PyTorch + GUI + SMS notification

A Django application developped for classification of a diabetes complication that affects eyes

Code for the paper "A Novel Hyperparameter Search Approach for Accuracy and Simplicity in Disease Prediction Risk Scoring".

A django based application to predict the severity of diabetic retinopathy

Code for the paper "nnMobileNet: Rethinking CNN for Retinopathy Research"

Medical Diagnosis using Contrastive Learning

CNN system analyzes retinal images & provides instant diagnoses, improving accuracy & reliability. The platform features a user-friendly interface implemented with Flask, allowing easy accessibility for users to upload images & to receive results.

Smart phone based automated diabetic retinopathy detection system

Reorganizing diabetes dataset, identifying patients with highest glucose levels, computing average A1C levels, determining disease duration, and exploring correlations between diabetic retinopathy and other variables

Diabetic Retinopathy Detection: Utilizing Multiprocessing for Processing Large Datasets and Transfer Learning to Fine-Tune Deep Learning Models

Papers and Public Datasets for Diabetic Retinopathy Detection

Diabetic Retinopathy Disease Classification using Deep Learning

Deep Learning & Labs Course, NYCU, 2023

Api to host Retfound

Classification of Fundus Images into 5 stages of Diabetic Retinopathy, and segmentation of blood vessels in fundus images

DIAGNOSIS OF DIABETIC RETINOPATHY FROM FUNDUS IMAGES USING SVM, KNN, and attention-based CNN models with GradCam score for interpretability,

Here, I have done my Final year Project on Kaggle Dataset