Automated Retinal Disease Classification Using Hybrid Transformer Model (svit) Using Optical Coherence Tomography Images

Optical coherence tomography (OCT) is a widely used imaging technique in ophthalmology for diagnosis and treatment. Recent advances in deep neural networks (DNNs) and vision transformers (ViTs) have paved the way for automated eye/retinal disease classifications and segmentations using OCT or spectral domain OCT (SD-OCT) images. Diabetic macular edema (DME), choroidal neovascularization (CNV), and Drusen are particularly challenging to accurately classify using OCT images because of their subtle differences and intricate features. Currently, the algorithms reported in the literature using DNNs or ViTs are computationally complex, consider fewer diseases, and are less accurate. This study proposes a hybrid SqueezeNet-vision transformer (SViT) model that combines the strengths of SqueezeNet and vision transformer (ViT), capturing local and global features of OCT images to achieve more accurate classification with less computational complexity. The proposed model uses the OCT2017 dataset for training, testing, and validation, and it performs both binary classification (normal vs disorders) as well as multiclass classification (DME, CNV, Drusen, and normal). As compared to state-of-the-art CNN-based and standalone Transformer models, the proposed SViT model achieves an overall classification accuracy of 99.90% for multiclass classification (CNV: 100%, DME: 99.9%, Drusen: 100%, and normal: 100%). With a good generalization ability, the model can be used to improve patient care and clinical decision-making across a broader range of applications.