Classification of Diabetic Retinopathy Stages Using Dual Attention Mechanism with EfficientNet-B0

Currently, Diabetic Retinopathy (DR) is one of the most common causes of blindness among people who suffer from diabetes for a long period.But, the existing detection models have drawbacks in classifying different stages of DR due to the similarity between some classes.To overcome this problem, a Dual Attention Mechanism with EfficientNet-B0 (DAM-EfficientNet-B0) is proposed for precise detection and classification of DR.Initially, retinal images are obtained from EyePACS and Messidor datasets and fed for preprocessing by Contrast Limited Adaptive Histogram Equalization (CLAHE) and data augmentation.After that, the preprocessed images are segmented based on a modified expectation maximization technique, while the segmented images are fed to the feature extraction process by ResNet-50.Finally, the extracted features are forwarded to the proposed classifier approach for accurate detection and classification of DR as per severity levels.The utilization of DAM helps select the relevant and important features which further leads to learning differences between classes.The experimental results show that DAM-EfficientNet-B0 attains an accuracy of 99.21% which is greater than the previous methods such as MultiStream-Deep Neural Network (MS-DNN), Concatenated Convolutional Neural Network (CCNN), and DNN-Butterfly Optimization Algorithm (DNN-BOA).