Kidney stone detection using an optimized Deep Believe network by fractional coronavirus herd immunity optimizer

• Published in: Biomedical signal processing and control Vol. 86; p. 104951
• Main Authors: Yan, Chaohua, Razmjooy, Navid
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023
• Subjects: Computer-aided diagnosis; Deep Believe Network; Fractional order version of coronavirus herd immunity optimizer; Kidney stone; Deep Believe Network; Fractional order version of coronavirus herd immunity optimizer; Computer-aided diagnosis; Kidney stone
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.104951

•Computer-aided kidney stone diagnosis from CT images based on deep believe network.•A modified Deep Believe Network (DBN) based on metaheuristic to get higher efficiency.•Using fractional order version of coronavirus herd immunity optimizer to provide the idea.•The method is validated by comparing with some published works from the literature. In this study, a computer-assisted kidney stone diagnosis system based on CT images has been proposed. The method is based on a combination of deep training and metaheuristics. The method aims to provide a customized Deep Believe Network (DBN) based on a fractional version of the coronavirus herd immunity enhancer to provide an efficient and reliable kidney stone diagnosis system. The designed method is then authenticated by running a standard benchmark called a “CT kidney dataset”. Subsequently, a comparison is made between the results and some other state-of-the-art methods. Simulations show that the recommended DBN/FO-CHIO outperforms the other studied approaches in terms of efficiency with an accuracy of 97.98%. Moreover, the proposed DBN/FO-CHIO recall outperforms others with 92.99%, demonstrating its excellent accuracy compared to other comparison algorithms. Moreover, the higher specificity of the proposed method compared to the other evaluated approaches indicates its advanced event-independent value.