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

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

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  • NETL
  • NSTL
  • Elsevier
© 2024 Elsevier LtdThe 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.

AlexnetDiagnosisExtreme learning machineImproved firebug swarm optimization algorithmKidney stone

Ding H.、Huang Q.、Razmjooy N.

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School of Information Engineering Nanyang Institute of Technology

Young Researchers and Elite Club Ardabil Branch Islamic Azad University||School of Information Engineering Nanyang Institute of TechnologySchool of Information Engineering Nanyang Institute of TechnologySchool of Information Engineering Nanyang Institute of Technology||Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS||||College of Technical Engineering The Islamic University||

2025

10.1016/j.bspc.2024.106898

Biomedical signal processing and control

Biomedical signal processing and control

SCI
ISSN：1746-8094
年,卷(期)：2025.99(Jan.)
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