Automated disease classification in retinal OCT images using deep learning
ZOU Tu
CAO Tingting
ZHANG Guojian
QIAN Lujia
LIN Lin
Abstract：Objective To develop an automated classification system for retinal diseases using optical coherence tomography(OCT)images.Methods A standardized dataset was constructed from both public repositories and clinical hospitals,comprising OCT images categorized into three classes:Normal,Polypoidal Choroidal Vasculopathy(PCV),and Diabetic Macular Edema(DME).Systematic preprocessing steps including data normalization,label verification,and augmentation were applied.Six convolutional neural network(CNN)architectures(VGG,ResNet,DenseNet,MobileNet,EfficientNet,Inception)were rigorously evaluated for classification performance.Results The VGG16 model achieved an accuracy of 98.92%,while ResNet50 and ResNet50_V2 attained 99.79%.Notably,ResNet50 exhibited marginally superior precision and recall compared to ResNet50_V2.DenseNet121 outperformed all other models,achieving an accuracy of 99.94%,precision of 99.90%,recall of 99.95%,and an F1-score of 0.999 2.Among EfficientNet variants,EfficientNetB0 and EfficientNetV2S reached accuracies of 99.75%and 99.83%,respectively.MobileNetV2 achieved the highest accuracy(99.85%)within its variants,followed by MobileNetV1(99.72%)and MobileNetV3(99.43%).Inception_V3 yielded an accuracy of 99.35%.Conclusion DenseNet121 demonstrates optimal performance for automated retinal OCT image classification,offering high accuracy and diagnostic efficiency for clinical applications.
Keywords：retinal diseasesoptical coherence tomographydeep learningimage classification
Publication Date：2025-06-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 227-232 )