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Cervical cancerous cell classification: opposition-based harmony search for deep feature selection

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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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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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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Nibaran Das & Bodhisatwa Mandal

  2. Applied AI Research Lab, Computer Science, University of South Dakota, Vermillion, SD 57069, USA

    KC Santosh

  3. Computer Vision Institute, Shenzhen University, 3688 Nanhai Blvd, Nanshan, Shenzhen, Guangdong Province, Shenzhen, China

    Linlin Shen

  4. Department of Pathology, Theism Medical Diagnostics Centre, Kolkata, 700030, India

    Sukanta Chakraborty

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  1. Nibaran Das
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Correspondence to KC Santosh.

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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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