Eye diseases classification with CNN using Pytorch 👁️

Eye Disease Classification using google images data

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.

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

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)

The Eye Disease Classification project aims to develop a robust model for the automated classification of retinal images . Leveraging a diverse dataset sourced from reputable repositories, the project employs a Convolutional Neural Network (CNN) architecture, with a focus on utilizing the pre-trained VGG19 model.

Eye Diseases classification using - CNN Model

Kod realizujący projekt w ramach zaliczenia przedmiotu Modelowanie Struktur i Procesów Biologicznych. Wykorzystuje prostą sieć konwolucyjną do klasyfikacji chorób oka na podstawie obrazów biomikroskopowych dna oka. Testowanie wykonano metodą walidacji krzyżowej. Projekt został zgłoszony jako artykuł na międzynarodową konferencję EMBS 2o23, Malta.

A deep learning model for classifying eye diseases using Convolutional Neural Networks (CNNs). Built with TensorFlow and Keras, trained on a medical eye dataset for accurate multi-class classification and analysis.

Detection and Classification of Eye Diseases: Diabetic Retinopathy, Cataract, and Glaucoma Using CNN

This approach combines CNNs, Attention-guided CNNs, and Vision Transformers (ViTs) for precise eye disease detection from retinal images. An Attention-guided CNN extracts disease-specific features, which fine-tune a Vision Transformer for classification. This process results in highly accurate and robust disease detection.

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CNN Based Eye Disease Classification

Retinal Disease Classification using EfficientNet architecture in CNN

CNN model for Eye disease classification from fundus images of eye

Kod realizujący projekt studencki przy współpracy z @pferst. Wykorzystuje prostą sieć konwolucyjną do klasyfikacji chorób oka. Testowanie wykonano metodą walidacji krzyżowej. Projekt został zgłoszony jako artykuł na międzynarodową konferencję EMBS 2o23, Malta.

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This notebook builds a deep learning model to classify eye diseases from images. It uses Convolutional Neural Networks (CNNs) and transfer learning with EfficientNetB3 to achieve accurate medical image classification.

CNN-based OCT Eye Disease Classification

CNN model for eye disease classification

This project was developed as part of the Biologically Inspired Artificial Intelligence course at the Silesian University of Technology. The goal is to automatically classify eye diseases (diabetic retinopathy, cataract, glaucoma, normal) from retinal images using a convolutional neural network (CNN) inspired by the human visual cortex.

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CLassify Eye Disease using CNN

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Eye disease classification CNN model with UI