Retinopathy Disease Detection and Classification Using a Coordinate Attention Module-Based Convolutional Neural Network with Leaky Rectified Linear Unit - Pravin Balaso Chopade; Prabhakar N. Kota; Bhagvat D. Jadhav; Pravin Marotrao Ghate; Shriram Sadashiv Kulkarni (International Islamic University Malaysia Engineering Journal)
The detection of Diabetic Retinopathy (DR) is an emergent research topic in recent decades, where DR is a primary cause of vision loss in humans. The existing techniques have limitations such as neuron death issues, vanishing gradient, and output offset. To overcome these issues, this paper proposes a Deep Learning (DL)-based technique for early and accurate DR detection. The Coordinate Attention Module (CAM) based Convolutional Neural Network (CNN) with Leaky Rectified Linear Unit (LReLU) is proposed for early and accurate detection of DR. The MESSIDOR dataset is preprocessed through the median filter to eliminate noise, and Contrast-Limited Adaptive Histogram Equalization (CLAHE) is utilized to increase the contrast level in an input image. The preprocessed images are given to Mayfly Optimization Algorithm-based Region Growing (MOARG) for image segmentation. Then, the features are extracted using ResNet50 and SqueezeNet, which extract deep learning features. The extracted features are given to CAM-based CNN with LReLU to detect DR, which overcomes the dead issues of neurons and minimizes the probability of inactive neurons. The proposed model achieves better results on the MESSIDOR datasets on the metrics of accuracy, precision, recall, specificity, f1-score, and Area Under Curve (AUC) values of about 99.72%, 99.46%, 99.25%, 99.61%, 99.37% and 99.14%, correspondingly, proving to be superior to the existing method, Capsule Network and Hybrid Adaptive DL based DR (HADL-DR).