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

Ashok Shanmugam; Kavitha Kvn; Prianka Ramachandran Radhabai; Senthilnathan Natarajan; Agbotiname Lucky Imoize; Stephen Ojo; Thomas I Nathaniel

doi:10.3389/fonc.2024.1264611

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

Front Oncol. 2024 May 1:14:1264611. doi: 10.3389/fonc.2024.1264611. eCollection 2024.

Authors

Ashok Shanmugam¹, Kavitha Kvn², Prianka Ramachandran Radhabai³, Senthilnathan Natarajan⁴, Agbotiname Lucky Imoize⁵, Stephen Ojo⁶, Thomas I Nathaniel⁷

Affiliations

¹ Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India.
² Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
³ Department of Artificial Intelligence and Machine Learning (AIML) New Horizon College of Engineering, Chennai, Tamil Nadu, India.
⁴ Department of Design and Automation, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
⁵ Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos, Nigeria.
⁶ Department of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, IN, United States.
⁷ School of Medicine Greenville, University of South Carolina, Greenville, SC, United States.

Abstract

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.

Keywords: GLCM; LBP; cervical cancer; classification; optimization.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Institute of Health (NIH) through the NIH R25 research grant and grant number R25AG067934-04.