An improved version of firebug swarm optimization algorithm for optimizing Alex/ELM network kidney stone detection

• Published in: Biomedical signal processing and control Vol. 99; p. 106898
• Main Authors: Ding, Huan, Huang, Qirui, Razmjooy, Navid
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: Alexnet; Diagnosis; Extreme learning machine; Improved firebug swarm optimization algorithm; Kidney stone; Alexnet; Diagnosis; Improved firebug swarm optimization algorithm; Extreme learning machine; Kidney stone
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2024.106898

•New method for diagnosing of the kidney stone.•Integrated Alexnet and ELM (Extreme Learning Machine) network is used for this purpose.•A newly improved version of firebug swarm optimization algorithm is used to optimize network.•The network is applied to the “CT Kidney Dataset” and validated by different research works. The use of CT scan to diagnose kidney stones is among the most accurate ways to confirm the presence of kidney stones in patients. The scan takes photographs inside the body using a computer and an X-ray. The present study proposes a new automatic methodology using an integrated Alexnet and ELM (Extreme Learning Machine) network to deliver more useful outcomes of detection for kidney stone. Afterward, the network is optimized on the basis of a newly improved version of firebug swarm optimization algorithm. The designed network is applied to the “CT Kidney Dataset”, and its outcomes are then verified by some different advanced procedures. The final results indicated that the proposed approach has better performance than the other methods.