A CNN-ViT Hybrid Architecture Res101-MViT-Ens for Accurate and Lightweight Automated Ocular Disease Diagnosis

Automated ocular disease diagnosis faces critical challenges including insufficient diagnostic precision, local–global feature imbalance, rigid feature fusion, weak cross-domain generalization, and difficult lightweight deployment. This study aims to develop a high-performance, generalizable, and deployable hybrid deep learning architecture for accurate multi-class ocular disease diagnosis. We propose the Res101-MViT-Ens hybrid architecture, which fuses ResNet101 for local fine-grained feature extraction and MobileViT-XXS for global contextual modeling via an end-to-end dynamic learnable weight fusion mechanism, with class-balanced sampling and medically adaptive augmentation for data preprocessing. The model is validated on the ODIR-5K dataset and cross-evaluated on three heterogeneous datasets (MESSIDOR-2, Kaggle DR, EyePACS). It achieves 99.44% accuracy, a 99.41% F1-score, and 99.32% Kappa on ODIR-5K, with a 99.46% average cross-dataset accuracy, outperforming state-of-the-art models. With 54 M parameters and 42.6 ms per-image inference latency on the Snapdragon 8 Gen2 edge module (Qualcomm Technologies, Inc., San Diego, CA, USA), it outperforms mainstream edge architectures. This proposed architecture achieves state-of-the-art diagnostic precision; balances accuracy, generalization and practicality; and is suitable for lightweight grassroots deployment in ocular disease screening.