谷歌INCEPTION-RESNET-V2迁移学习实现图像二分类判断图像是否生病

keras densenet40层网络结构，实现眼底图二分类。

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).

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)

Binary classification on fundus images to grade Diabetic Retinopathy (DR). Trained on EyePACS, APTOS, Messidor datasets using PyTorch. Implements InceptionV3, ResNet, EfficientNet, and Vision Transformer (ViT) models. Provides scripts for training, evaluation, and prediction.

Binary classification of Diabetic Retinopathy using SVM in MATLAB with fundus image features.

CNN replication of Abed et al. (2020) for diabetic retinopathy detection on fundus images (DiaretDB0/1, DRIMDB). Binary healthy/pathological classification with CLAHE preprocessing, extended to haemorrhage detection. Critical analysis of class imbalance and model bias in small medical datasets.

Built a deep learning model to perform multi-label disease classification on retinal fundus images using VGG16 transfer learning. Employed data augmentation, regularization, and TPU strategy for scalable and accurate training. Evaluated using AUROC and binary accuracy, with further interpretability achieved via LIME image explanations

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An AI-based systems for Laterality binary-class classification using supervised deep learning methodology. Data are retinal fundus images.

An AI-based systems for FOV binary-class classification using supervised deep learning methodology. Data are retinal fundus images.

An AI-based systems for ARMD binary-class classification using supervised deep learning methodology. Data are retinal fundus images.

Binary classification of Diabetic Retinopathy from color fundus photographs using CNNs in PyTorch

Binary model for age-related macular degeneration (AMD) classification from fundus images (DFI).

Test/Inference ARMD binary-class classification AI-based supervised deep learning model. Data are retinal fundus images.

Heatmaps are generated using ARMD AI model. The model was trained, validated and tested for binary classification using retinal fundus images using deep learning (DL) methodology. Data are retinal fundus images.

Automated Diabetic Retinopathy detection from fundus images using custom-built CNN; Multiclass (74% acc) & Binary (95% acc) classification on APTOS 2019 dataset.

Glaucoma detection from Deep Fundus Images using EfficientNet-B3 — binary classification of Glaucomatous Optic Neuropathy (GON+ vs GON-) on the HYGD dataset.

A comparison of two state-of-the-art deep learning architectures—EfficientNetB0 and DenseNet121—for the binary classification of diabetic retinopathy from color fundus photographs.

This project aimed at developing a system that can identify eye diseases from fundus images. My project focuses on binary classification of uveitis. which consists of Cataract and Glaucoma, The project uses Convolutional Neural Networks (CNN) as a ML technique to extract features from the images & Gradient Boosting used for classification

Traditional ML baselines for ROP classification using retinal fundus images. Includes HOG+SVM and Sobel+SVM pipelines for binary and multi-class setups. This repo is the first phase of a larger project focused on building deep learning models to automatically grade ROP stages from clinical retinal images.

APTOS 2019 had released a dataset on fundus images containing the stages of Diabetic Retinopathy as No_DR, mild, moderate, severe and Proliferative_DR. Have used the same dataset, balanced it as No_DR and DR. Then if detected as DR it'll classify the 4 stages. The binary classification was to balance the dataset. Trained with VGG16 and MobileNetV2.