ArticleScientific reports2025
Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparative Analysis of CNN and Transformer Models for Multi-Class Diabetic Retinopathy Grading Using Fundus Images.Diagnostics (Basel, Switzerland) · 2026Article
- A Text-Guided Lesion Mining Vision-Language Ordinal Classification Framework for Diabetic Retinopathy Grading.Journal of imaging · 2026Article
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4 authors.
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Abstract
An Attention Dual Transformer with Adaptive Temporal Convolutional (ADT-ATC) model is proposed in this research work for enhanced detection of Diabetic Retinopathy (DR) from retinal fundus images. Unlike traditional methods which evolved so far in DR analysis, the proposed model specifically processes the multi-scale spatial features through dual spatial transformer network and captures the temporal dependencies through adaptive temporal convolutional unit. The fine patterns like microaneurysms, and larger anatomical regions, including hemorrhages are focused on dual spatial transformer block which provides comprehensive and detailed analysis of spatial features. Additionally, a hierarchical cross attention module is included to fuse the spatial and temporal features which is essential to identify the DR. Experimentation of the proposed model using DRIVE and Diabetic Retinopathy datasets demonstrates the better performance of proposed ADTATC model with an accuracy of 98.2% on DRIVE and 97.7% on Diabetic Retinopathy datasets compared to conventional deep learning models.
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