TL-MED: Multiclass eye disease classification based on ensemble transfer learning and CRVO-BRVO detection via a single shot multibox detector.

Objective: To address the limitations of traditional approaches in diagnosing eye diseases, we propose five state-of-the-art, transfer-learning-based deep convolutional neural network (DCNN) models and an ensemble model. These models are trained on thousands of retinal images to achieve robust classification of diseases such as glaucoma, cataracts, and diabetic retinopathy. Methods: Our dataset consists of 3744 raw retinal images. We implemented and fine-tuned five individual DCNN models: VGG16, ResNet152, DenseNet169, EfficientNetB3, and NASNetMobile. We also developed an ensemble model that combines ResNet152, DenseNet169, and EfficientNetB3. Additionally, a single-shot multibox detector (SSD) was used for the detection of central and branch retinal vein occlusions (CRVO and BRVO). Results: Among the individual models, DenseNet169 demonstrated superior performance, with 96% accuracy and 22% loss. The NASNetMobile model achieves the lowest accuracy at 87%. The proposed ensemble model outperforms all the individual networks, reaching a peak accuracy of 97%. These results highlight the effectiveness of transfer learning in improving classification accuracy. Conclusion: The proposed AI-driven approaches provide a reliable solution for the early and precise detection of common eye diseases. By leveraging advanced deep learning techniques, our work contributes to the medical field by assisting healthcare professionals in diagnosis and paving the way for improved diagnostic tools and enhanced patient care.