HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images.

Autor:	Shanmugam A; Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India., Kvn K; Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India., Radhabai PR; Department of Artificial Intelligence and Machine Learning (AIML) New Horizon College of Engineering, Chennai, Tamil Nadu, India., Natarajan S; Department of Design and Automation, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India., Imoize AL; Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos, Nigeria., Ojo S; Department of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, IN, United States., Nathaniel TI; School of Medicine Greenville, University of South Carolina, Greenville, SC, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2024 May 01; Vol. 14, pp. 1264611. Date of Electronic Publication: 2024 May 01 (Print Publication: 2024).
DOI:	10.3389/fonc.2024.1264611
Abstrakt:	Cervical cancer is a significant concern for women, necessitating early detection and precise treatment. Conventional cytological methods often fall short in early diagnosis. The proposed innovative Heap Optimizer-based Self-Systematized Neural Fuzzy (HO-SsNF) method offers a viable solution. It utilizes HO-based segmentation, extracting features via Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The proposed SsNF-based classifier achieves an impressive 99.6% accuracy in classifying cervical cancer cells, using the Herlev Pap Smear database. Comparative analyses underscore its superiority, establishing it as a valuable tool for precise cervical cancer detection. This algorithm has been seamlessly integrated into cervical cancer diagnosis centers, accessible through smartphone applications, with minimal resource demands. The resulting insights provide a foundation for advancing cancer prevention methods. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Shanmugam, KVN, Radhabai, Natarajan, Imoize, Ojo and Nathaniel.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu