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Automated Diabetic Retinopathy Screening in Resource-Limited Areas with Attention-Enhanced Deep Learning on Fundus Images

Binusha Sornil A.*, Sheeja Herobin Rani C.**, Rexiline Sheeba I.***, Renisha G.****

* Department of Computer Science and Engineering, Stella Mary’s College of Engineering, Kanniyakumari, Tamil Nadu, India.

** Department of Electronics and Communication Engineering, St. Xavier's Catholic College of Engineering, Kanniyakumari, Tamil Nadu, India.

*** Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.

**** Department of Electronics and Communication Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi, Tamil Nadu, India.

Periodicity:October - December'2024
DOI : https://doi.org/10.26634/jip.11.4.21511

Abstract

Diabetic retinopathy (DR) is a leading contributor to vision impairment, particularly in areas with limited resources where access to specialized care is scarce. This study introduces an automated screening system for DR using attention- enhanced deep learning on retinal fundus images, specifically designed for these regions. The system leverages convolutional neural network (CNN) technology with integrated attention mechanisms to focus on critical features indicative of DR, such as microaneurysms and hemorrhages, improving detection accuracy and reliability. Varied retinal fundus images were used for training and validation, with data augmentation applied to enhance model robustness. The model was optimized for deployment on low-cost hardware, ensuring feasibility in resource-limited settings. Performance evaluation demonstrated high sensitivity and specificity, and attention maps provided interpretability for healthcare providers. This automated system has the potential to enhance early detection of diabetic retinopathy (DR) in underserved areas, facilitating timely intervention and reducing the risk of blindness. By making advanced diagnostic tools accessible, this approach promotes equitable healthcare and helps to prevent vision loss globally.

Keywords

Diabetic Retinopathy, Deep Learning, Convolutional Neural Networks, Microaneurysms, Retinal Fundus Images, Hemorrhage.

How to Cite this Article?

Sornil, A. B., Rani, C. S. H., Sheeba, I. R., and Renisha, G. (2024). Automated Diabetic Retinopathy Screening in Resource-Limited Areas with Attention-Enhanced Deep Learning on Fundus Images. i-manager’s Journal on Image Processing, 11(4), 10-19. https://doi.org/10.26634/jip.11.4.21511

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Automated Diabetic Retinopathy Screening in Resource-Limited Areas with Attention-Enhanced Deep Learning on Fundus Images

Abstract

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