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Cervical cancerous cell classification: opposition-based harmony search for deep feature selection.
- Source :
- International Journal of Machine Learning & Cybernetics; Nov2023, Vol. 14 Issue 11, p3911-3922, 12p
- Publication Year :
- 2023
-
Abstract
- Over 500 K (per year) cervical cancer cases are reported with a high mortality rate (6–9%). Automatically detecting cervical cancer using the Computer-Aided Diagnosis (CAD) tool at an early stage is important since it leads to successful treatment as pathologists. In this paper, we propose a tool that classifies cervical cancer cases from Pap smear cytology images using deep features. The proposed tool constitutes a Convolutional Neural Network (CNN) and a metaheuristic evolutionary algorithm called Opposition-based Harmony Search Algorithm (O-bHSA) for deep feature section. These features are classified using standard classifiers: SVM, MLP, and KNN. On two different publicly available datasets: Pap smear and liquid-based cytology, the proposed tool outperforms not only seven well-known optimization algorithms but also state-of-the-art methods. Codes are publicly available on GitHub. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18688071
- Volume :
- 14
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- International Journal of Machine Learning & Cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 172360492
- Full Text :
- https://doi.org/10.1007/s13042-023-01872-z