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.
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Discover the latest articles, news and stories from top researchers in related subjects.Data availability
In the present work, we have used two datasets, (a) Mendeley Liquid-Based Cytology (MLBC) [34] and (b) SIPaKMeD Pap Smear dataset [16]. The data sets are publicly availble at “https://data.mendeley.com/datasets/zddtpgzv63/4” and “https://www.cs.uoi.gr/~marina/sipakmed.html” respectively.
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Acknowledgements
The work is supported by SERB (DST), Govt. of India (Ref# EEQ/2018/000963). Authors are also thankful to Akil Kadolia and Soumyajyoti Dey for their efforts during the development of different modules.
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Das, N., Mandal, B., Santosh, K. et al. Cervical cancerous cell classification: opposition-based harmony search for deep feature selection. Int. J. Mach. Learn. & Cyber. 14, 3911–3922 (2023). https://doi.org/10.1007/s13042-023-01872-z
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DOI: https://doi.org/10.1007/s13042-023-01872-z