Detection of COVID‐19 patient based on attention segmental recurrent neural network (ASRNN) Archimedes optimization algorithm using ultra‐low‐dose CT images

Abstract

SUMMARYIn this article, the detection of COVID‐19 patient based on attention segmental recurrent neural network (ASRNN) with Archimedes optimization algorithm (AOA) using ultra‐low‐dose CT (ULDCT) images is proposed. Here, the ultra‐low‐dose CT images are gathered via real time dataset. The input images are preprocessed with the help of convolutional auto‐encoder to recover the ULDCT images quality by removing noises. The preprocessed images are given to generalized additive models with structured interactions (GAMI) for extracting the radiomic features. The radiomic features, such as morphologic, gray scale statistic, Haralick texture are extracted using GAMI‐Net. The ASRNN classifier, whose weight parameters optimized with Archimedes optimization algorithm enables COVID‐19 ULDCT images classification as COVID‐19 or normal. The proposed approach is activated in MATLAB platform. The proposed ASRNN‐AOA‐ULDCT attains accuracy 22.08%, 24.03%, 34.76%, 34.65%, 26.89%, 45.86%, and 32.14%; precision 23.34%, 26.45%, 34.98%, 27.06%, 35.87%, 34.44%, and 22.36% better than the existing methods, such as DenseNet‐HHO‐ULDCT, ELM‐DNN‐ULDCT, EDL‐ULDCT, ResNet 50‐ULDCT, SDL‐ULDCT, CNN‐ULDCT, and DRNN‐ULDCT, respectively.