Micro aneurysm detection using optimized residual-based temporal attention Convolutional Neural Network with Inception-V3 transfer learning

In this manuscript micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) with Inception-V3 transfer learning optimized with equilibrium optimization algorithm (MA-RTCNN-Inception V3-EOA) is proposed. The proposed research work contains four phases: (1) pre-processing, (2) segmentation, (3) post-processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA-RTCNN-Inception V3-EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f-measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis. The experimental outcome demonstrates that the proposed method provides 23.56%, 14.99%, and 21.37% higher accuracy and 31.26%, 57.69%, and 21.14% minimum classification error rate compared to existing methods, such as diabetic retinopathy identification utilizing prognosis of micro aneurysm and early diagnosis for non-proliferative diabetic retinopathy depending on deep learning approaches (DRD-CNN-NPDR), a magnified adaptive feature pyramid network for automatic micro aneurysms identification (MAFPN-AMD-MAFP-Net) respectively. RESEARCH HIGHLIGHTS: Micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) is proposed. To get rid of the retina background, guided box filtering is applied. COAT is used for segmenting the images into smaller parts RTCNN is used for accurate micro aneurysms disease classification. RT-CNN algorithm successfully identifies the micro aneurysms using EOA.