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

Nibaran Das, Bodhisatwa Mandal, Kc Santosh, Linlin Shen, Sukanta Chakraborty

Research output: Journal PublicationArticlepeer-review


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 .

Original languageEnglish
Pages (from-to)3911-3922
Number of pages12
JournalInternational Journal of Machine Learning and Cybernetics
Issue number11
Publication statusPublished - Nov 2023
Externally publishedYes


  • CNN
  • Cervical cancer
  • Deep features
  • Opposition-based harmony search

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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