Work

Multi-task deep learning framework combining CNN: vision transformers and PSO for accurate diabetic retinopathy diagnosis and lesion localization

Year: 2025

Type: article

Abstract: Diabetic Retinopathy (DR) continues to be the leading cause of preventable blindness worldwide, and there is an urgent need for accurate and interpretable framework. A Multi View Cross Attention Vision Transformer (MVCAViT) framework is proposed in this research paper for utilizing the information-complementarity between the dually available macula and optic disc center views of two images from the DRTiD dataset. A novel cross attention-based model is proposed to integrate the multi-view spatial and contextual features to achieve robust fusion of features for comprehensive DR classification. A Vision Transformer and Convolutional neural network hybrid architecture learns global and local features, and a multitask learning approach notes diseases presence, severity grading and lesions localisation in a single pipeline. Results show that the proposed framework achieves high classification accuracy and lesion localization performance, supported by comprehensive evaluations on the DRTiD dataset. Attention-based visualizations further enhance interpretability, indicating the framework's potential for clinical use. This framework establishes a criterion for improving state-of-the-art retinal image analysis for DR diagnosis which may result in better patient results and final clinical decision.

Source: Scientific Reports

DOI: 10.1038/s41598-025-18742-z

Authors: S. Vijayalakshmi, J. Samuel Manoharan, B. Nivetha, A. Sathiya

Institutions: Sona College of Technology, Jerusalem College of Engineering, M. Kumarasamy College of Engineering

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690