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

Frontiers in Oncology (May 2024)

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

Affiliations

Ashok Shanmugam: Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
Kavitha KVN: Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Prianka Ramachandran Radhabai: Department of Artificial Intelligence and Machine Learning (AIML) New Horizon College of Engineering, Chennai, Tamil Nadu, India
Senthilnathan Natarajan: Department of Design and Automation, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Agbotiname Lucky Imoize: Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos, Nigeria
Stephen Ojo: Department of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, IN, United States
Thomas I. Nathaniel: School of Medicine Greenville, University of South Carolina, Greenville, SC, United States

DOI: https://doi.org/10.3389/fonc.2024.1264611
Journal volume & issue: Vol. 14

Abstract

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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.

Published in Frontiers in Oncology

ISSN: 2234-943X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Medicine: Internal medicine: Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Website: https://www.frontiersin.org/journals/oncology/

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