Built an image classification model using MobileNetV3-Large to analyze Optical Coherence Tomography (OCT) images and detect the presence of eye disease. Deployed the model as a Streamlit web application for real-time image upload and prediction.

Multi-Label Classification of Eye Diseases

Eye diseases classification with CNN using Pytorch 👁️

AI-driven Detection & Classification of Diabetic Retinopathy" is a cutting-edge approach that combines artificial intelligence (AI) with medical image analysis to identify and categorize a specific eye disease called Diabetic Retinopathy (DR).

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.

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.

Focuses on classifying eye diseases into four categories: normal, cataract, diabetic retinopathy, and glaucoma. Using a dataset of over 4,000 images, the model achieved an accuracy of 92%. The project involved data augmentation and fine-tuning techniques to improve model performance and ensure robustness.

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.

research on the RETFound retinal disease foundational model, focused on adapting it for the ODIR 2019 multi-label eye disease classification challenge

Official code for Multimodal Classification of Alzheimer’s Disease by Combining Facial and Eye-Tracking Data.

Research Project: A multimodal AI assistant for eye disease classification and explanation, integrating text-based symptom queries, OCT/eye image analysis, and multimodal fusion with a ChatGPT-style interface.

The first year after diagnosis is a crucial time for patients with Type 2 diabetes. While it’s always important to maintain healthy blood sugar levels, new research shows that better control during the first year can reduce the future risk for complications, including kidney disease, eye disease, stroke, heart failure and poor circulation to the limbs. Diabetes, often referred to by doctors as diabetes mellitus, describes a group of metabolic diseases in which the person has high blood glucose (blood sugar), either because insulin production is insufficient, or because the body's cells do not respond properly to insulin, or both. This project helps in identifying whether a person has diabetes or not, if predicted diabetic the project suggests measures for maintaining normal health and if not, diabetic it predicts the risk of getting diabetic. In this project Classification algorithm was used to classify the Pima Indian diabetes dataset. Results have been obtained using Web Application.

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.

This is my university research project repository. This will contains all the things that i do for my research project.

This project builds a CNN model to classify retinal images into four categories: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. The dataset includes around 1000 images per class from sources like IDRiD and HRF. The goal is to aid in early detection and treatment planning for retinal diseases.

Diabetic Retinopathy (DR) is one of the eye-related disease that reduces the integrity of the blood vessels in the retinal layers which leads to retinal blood vessel leakage [2]. Sodium Fluorescein Angiography (FA) is widely used to monitor the leakage or the permeability of the vessel by imaging the back of the eyes as an important diagnostic value. Gamez [2] which is a PhD student in University of Bristol started FA on mice. Gamez [2] manually extracted fluorescent intensity data from the resulting FA videos and a graph of the fluorescence intensity ratio (FIR) versus time was plotted to obtain the gradient which is the solute flux (ΔIf/ Δt). These data was then used to assist the development of the Fick's Law adapted equation P=ΔIf/ Δt /(ΔC × A) to obtain the permeability of the vessel. The obstacle of this method was the manual data capturing process was too time consuming. This method also requires a lot of manual adjustments due to the movement of the camera caused by the heartbeat of the mice and their eyeball motion. The movement of the camera also caused blurry and unsharp images in the FA videos which led to inaccurate fluorescent intensity. A more intelligent way of data capturing was developed in this project using openCV with Python. This project firstly experimented on using K-means clustering to segment the exchange vessel groups and the large vessel group out of the FA frames to obtain the FIR for the FIR vs time graph. This project experimented on the two settings of K-mean clustering. One used random initial centers and the other one used the previous frame’s centers found by K-means clustering as the initial centers of the K-means clustering for the current frame (Reuse center K-means clustering). The experiment found that the random initial centers K-means clustering output stable FIR when the maximum iteration was 7 or above and the best epsilon (specific accuracy) was 0.1. Maximum iteration below 7 cannot be used due to FIR vs time graph showed large amount of noise and severe deformation. Conversely, the reuse center K-means clustering showed no deformation and noise on the FIR vs time graph when the maximum iteration was 7 or below and a much shorter execution time than the random initial centers K-means clustering. Then, the difference on the gradient of the FIR vs time graph was further examine between the two K-means clustering setting. The random initial centers Kmeans clustering showed fluctuation on the gradient value when the maximum iteration was between 7 and 15. The reuse center K-means clustering showed either an ascending or descending trend on the gradient value when the maximum iteration was below 7 and the gradient value stabilized when the maximum iteration was between 7 and 15. Reuse center K-means clustering was decided to implement in the final software and maximum iteration 7 was set as default to prioritize gradient accuracy over the execution time, and allow user to lower the maximum iteration to reduce execution time. This project then experimented on blurry frame classification by using Sobel edge detection. Convolution was performed with Sobel derivative operator on each FA frame to obtain an edge sharpness value. The edge sharpness versus frame number graphs were examined for all video and discovered a great separation between sharp and blurry frames in edge sharpness value. Sharp frames had higher edge sharpness and blurry frames had lower edge sharpness. A piece of code was created to loop along all the data points in edge sharpness vs frame number graph to classify sharp and blurry frames. The code firstly checked if the range of several neighboring data point (PtPbox) is larger than a specific value (tolerance value), then the data point needed a sharpness check, which take the mean of several neighboring data point (meanBox) and check is the current data point edge sharpness is lower or higher than the mean value. Lower means blurry frame and higher means sharp frames. A series of experiments were performed and the optimal value for PtPbox is 20, the meanBox is 10, the tolerance value is 0.1 and no histogram equalization is required. The sharp frames identification accuracy was above 80% and the blurry frames identification accuracy was above 96% for all the tested FA videos. All these experimental codes were then connected by a graphical user interface based on python with PyQT4. Finally, the PyInstaller was used to package these Python codes into a stand-alone Microsoft Window executable for Gamez [2] to use.

This repository will attempt to classify eye disease images using Few Shot Learning (FSL)

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A deep learning model that can detect macular degeneration and diabetic macular edema by optical coherence tomography method will be realized.

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Eye Diseases classification using - CNN Model

Eye disease classification involves the application of machine learning and deep learning techniques to accurately identify and categorize various eye diseases from medical images, such as retinal scans, fundus images This project aims to automate the diagnosis process, assisting ophthalmologists in early detection and effective treatment planning.

Upgraded an image processing app using deep learning to predict Diabetic Retinopathy, Glaucoma, or Cataract from eye scans with 96% accuracy. Used Python3, Jupyter Notebook, and Git.

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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.