Attention Based Deep Learning Framework to Recognize Diabetes Disease From Cellular Retinal Images

Biochem Cell Biol. 2023 Jul 20. doi: 10.1139/bcb-2023-0151. Online ahead of print.ABSTRACTA medical disorder known as diabetic retinopathy (DR) affects people who suffer from diabetes. Globally many people visually impaired and blind due to diabetic retinopathy. The primary cause of DR in diabetic patients is high blood sugar and it affects blood vessels available in the retinal cell. The recent advancement in deep learning and computer vision methods, and its automation applications able to recognize present of DR in retinal cells and vessel images. Authors have proposed Attention-based hybrid model to extract features. Proposed methodology uses DenseNet121 architecture for convolution learning and then the feature vector will be enhanced with channel and spatial attention model. The proposed architecture also simulates for binary and multiclass classification to recognized infection and spreading of disease. Binary classification recognize DR images either positive or negative, while multiclass classification represents an infection in a scale of 0 to 4. Simulation of the proposed methodology has achieved 98.57 % and 99.01% accuracy for multiclass and binary classification, respectively. Simulation of the study also explored the impact of data augmentation to make the proposed model robust and generalized. Attention based deep learning model has achieve remarkable accuracy to detect diabetic infection from retinal cellular images.PMID:37473447 | DOI:10.1139/bcb-2023-0151

Source: Biochemistry and Cell Biology - July 20, 2023 Category: Biochemistry Authors: Deep Kothadiya Amjad Rehman Sidra Abbas Faten S Alamri Tanzila Saba Source Type: research