Innovations in Science and Technologies Ҷилди 3 № 2 (2026) · Саҳифаҳои 67-79
RESEARCH ON HYBRID ALGORITHMS FOR DIAGNOSING EYE DISEASES
Iskandarova, Sayyora, Iskandarova, Feruza, Eraliyev, Seitjan
Аннотатсия
To develop and rigorously evaluate a novel hybrid deep learning framework for simultaneous diagnosis of four critical ocular conditions, more precisely: cataract, diabetic retinopathy, glaucoma, and normal fundus - using a relatively small but balanced dataset of fundus images. The study addresses the challenge of achieving high diagnostic accuracy with limited data through architectural innovation and optimized training protocols. We propose a parallel hybrid convolutional neural network that integrates EfficientNetB3 (for global contextual feature extraction) and DenseNet121 (for local detailed feature extraction). The model processes dual-resolution inputs (300×300 and 224×224 pixels) simultaneously. A novel two-phase training strategy was implemented: Phase 1 (10 epochs) with frozen ImageNet-pre-trained backbones to train only the newly added classification heads, followed by Phase 2 (15 epochs) with selective fine-tuning of upper layers. The model incorporated label smoothing (ε=0.05), L2 regularization, and dropout to combat overfitting. The dataset comprised 3,200 curated fundus images (800 per class), split into training (2,560), validation (320), and test (320) sets. The hybrid model achieved a peak validation accuracy of 92.19% and a test accuracy of 91.87%, significantly outperforming standalone EfficientNetB3 and DenseNet121 models (p<0.001, McNemar's test). Diabetic retinopathy was detected with nearperfect precision (98.75%), while cataract, glaucoma, and normal classes showed robust and balanced performance. The proposed parallel hybrid architecture, combined with a disciplined twophase training regimen, successfully overcomes the limitations of small medical datasets. It effectively leverages complementary feature hierarchies from two state-of-the-art networks, establishing a new benchmark for multi-class ocular disease diagnosis. This work demonstrates that architectural synergy and meticulous training design can yield clinically relevant accuracy without requiring prohibitively large datasets.
Ocular Disease DiagnosisDeep LearningHybrid Neural NetworksEfficientNetDenseNetFundus ImagingMulti-class ClassificationSmall Dataset Learning
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