Automated machine learning can facilitate the early diagnosis and timely treatment of diabetic retinopathy. Following the 1st Diabetic Retinopathy: Segmentation and Grading Challenge held with ISBI in 2018, we would like to promote the progress further through 2nd challenge using a new dataset, Deep Diabetic Retinopathy Image Dataset (DeepDRiD). The challenge is subdivided into three tasks as follows: A. Dual-View Disease Grading: Classification of fundus images according to the severity level of diabetic retinopathy using dual view retinal fundus images. B. Image Quality Estimation: Fundus quality assessment for overall image quality, artifacts, clarity, and field definition. C. Transfer Learning: Explore the generalizability of a Diabetic Retinopathy (DR) grading system. The robust and generalizable models are expected to be developed to solve clinical issues in reality.

[MICCAI 2023] Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images

[TMI 2024] Code for "Concept-based Lesion Aware Transformer for Interpretable Retinal Disease Diagnosis"

Multi-Label Classification of Eye Diseases

Late AMD progression classification using fundus images and genotypes

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

Source code for GARDNet: Robust Multi-View Network for Glaucoma Classification in Color Fundus Images

Explainable deep learning approach for Diabetic Retinopathy severity classification from retinal fundus images.

A CNN based deep learning model to detect and classify eye disease from the fundus images. Ensemble learning and different CNN architecture is used for the accurate classification.

Automated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of annotations, which is the fundamental motivation of this work. In this study, we construct a dataset containing 1219 fundus images (from DR patients and healthy controls) with annotations of exudate lesions. In addition to exudate annotations, we also provide four additional labels for each image: leftversus- right eye label, DR grade (severity scale) from three different grading protocols, the bounding box of the optic disc (OD), and fovea location. This dataset provides a great opportunity to analyze the accuracy and reliability of different exudate detection, OD detection, fovea localization, and DR classification algorithms. Moreover, it will facilitate the development of such algorithms in the realm of supervised and semi-supervised learning.

Evaluation of the segmented vascular structures of the retina of our eye obtained through fundus photography using Machine learning techniques. Two open-source databases of the retinal images (DRIVE and STARE) are used. K - Means Clustering Algorithm is used for the segmentation of the retinal images. MATLAB r2020b environment was employed for feature extraction and image segmentation. The accuracy of the segmentation is evaluated for both the database as well as the algorithms. A simple GUI was developed for convinience of evaluation. The future work in this project deals with improving the accuracy of the segmented vessels and using them for classification of vessels into arteries and veins, and also for identification of various diseases like Diabetic Retinopathy, Stroke, Glaucoma etc. A more interactive, highlyautomated graphical user interface (GUI) may also be developed for user convenience and the software may be made compatible for various devices in the future.

Multiclass classification of eye diseases based on eye fundus images using CNNs

Classification of Ocular Diseases based on Eye Fundus Images.

This project implements a deep learning solution for detecting various eye diseases from fundus images. The project includes comprehensive data analysis, preprocessing, and multi-label classification of eye conditions.

Eye Disease Diagnosis

The project addresses automatic detection of microaneurysms (MA) which are first detectable changes in Diabetic Retinopathy (DR). Green channel, being the most contrasted channel, of the color fundus images are considered. The algorithm includes pre-processing, MA candidates detection, features extraction, classification and comparison with ground truth to evaluate the performance of classifier model.

Deep learning pipeline for classification of Cataract, Diabetic Retinopathy, Glaucoma and Normal using fundus images

EfficientNet-based diabetic retinopathy classification with interpretability for retinal fundus images

Python Notebook on Classification of eye fundus images from the IDRiD Dataset based on severity of Diabetic Retinopathy.

Glaucoma Detection and Classification using Deep Learning Glaucoma is a condition of eye in which optic nerve is damaged due to abnormally high pressure in the eye. It is a chronic and irreversible disease. It is one of the leading cause of blindness across the globe in people over the age of 60. There is no cure for glaucoma, but early detection and medical treatment can prevent from disease progression. A goal of this project was to use deep learning architecture to build a model to detect and classify glaucoma by combining multiple deep features. Keras was used to build the model. We used publicly available database Drishti-GS1. Methodology: This project was divided into two parts: Glaucoma Detection First, ROI (Region of interest) which is an area where optic disc and cup are located in the center and blood vessels of the Glaucoma fundus images were extracted using U shape convolutional neural network and then cup to disc ratio was calculated to classify if the image was glaucomatous or normal. This Paper was used for ROI extraction and disc segmentation. Glaucoma Classification Cup to disc ratio was used for glaucoma classification. VGG16 CNN model was used to distinguish between glaucoma and non-glaucoma related images from fundus images. Glaucoma severity can also be classified from cup to disc ratio: Mild ( CDR >0.3 and <0.5) Moderate (CDR >=0.5 and <0.8) Severe (CDR >=0.8)

A multilabel classification method using ensembled vision transformers for single-eye images trained on the ODIR-2019 challenge dataset.

Interpretable gender classification from retinal fundus images using BagNets

We combined the image enhancement algorithm with transfer learning to achieve the classification of color fundus photo.

VO and PVD classification

The project identifies the glaucoma and non-glaucoma patients by taking fundus - eye scan images as inputs and this project is made using Deep Learning techniques of binary classification through convolutional neural networks(CNN).

This project explores automated detection and classification of Diabetic Retinopathy (DR) using deep learning techniques on retinal fundus images. It aims to develop robust and generalizable models for early diagnosis and severity grading of DR, supporting ophthalmologists in clinical screening.

Diabetic Retinopathy - Detecting Diabetes (and its severity) in patients from their fundus image of their retina. Project uses Convolution Neural Networks to achieve the goal of binary classification (aiming for multiclass clasification if results are desirable)

Multiple eye disease classification from FUNDUS retina images using deep ensemble model.

All the models we use for the classification of OCT images, eye fundus and spectral images

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