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A multi-resolution convolutional attention network for efficient diabetic retinopathy classification

Publication Type : Journal Article

Publisher : Elsevier BV

Source : Computers and Electrical Engineering

Url : https://doi.org/10.1016/j.compeleceng.2024.109243

Keywords : Computer-aided diagnosis, Convolutional neural network, Diabetic retinopathy, Multi-dilation attention block, Multi-resolution convolutional attention network, Support vector machine

Campus : Bengaluru

School : School of Artificial Intelligence

Year : 2024

Abstract : Diabetic retinopathy (DR) is a disorder commonly associated with individuals who have diabetes and is caused by excessive glucose levels in the blood, which induce retinal damage. Manual diagnosis of DR by medical experts is time-consuming; hence, a computer-aided diagnosis (CAD) solution for faster identification and improved efficiency is required. Diagnosis of DR is complex due to the prevalence of inter-class differences and small lesions, making it difficult for convolutional neural networks (CNNs) to identify discriminative regions effectively. The most critical challenge is locating complex patterns connected with visual differences and lesion locations. To tackle the aforementioned challenges, this work presents a novel multi-resolution convolutional attention network (MuR-CAN), which improves overall performance by emphasizing discriminative features. MuR-CAN includes a multi-dilation attention block (MDAB), which uses depth-wise convolution layers with various dilation rates to allow the model to prioritize relevant features and capture multi-scale spatial information. This method provides for simultaneously highlighting fine features and subtle visual patterns. Furthermore, the features extracted from the MuR-CAN are utilized to train the support vector machine (SVM) for DR classification. The integration of the SVM classifier with the proposed CNN architecture ensures improved accuracy. The experimental findings demonstrate that the proposed model outperforms the current state-of-the-art methods.

Cite this Research Publication : Sandeep Madarapu, Samit Ari, Kamalakanta Mahapatra, A multi-resolution convolutional attention network for efficient diabetic retinopathy classification, Computers and Electrical Engineering, Elsevier BV, 2024, https://doi.org/10.1016/j.compeleceng.2024.109243

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