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Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

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Abstract

Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes deep learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts deep feature from several convolution neural network (CNN) models and uses a two-step feature reduction approach to ensure reduction in computation cost and faster convergence. The features extracted from the CNN models form a large feature space whose dimensionality is reduced using principal component analysis while preserving 99% of the variance. A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the grey wolf optimizer, thus improving the classification performance. Finally, the selected feature subset is used to train an support vector machine classifier for generating the final predictions. The proposed framework is evaluated on three publicly available benchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev Pap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5-class) dataset achieving classification accuracies of 99.47, 98.32 and 97.87%, respectively, thus justifying the reliability of the approach. The relevant codes for the proposed approach can be found in: https://github.com/DVLP-CMATERJU/Two-Step-Feature-Enhancement.

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Acknowledgements

The work is supported by SERB (DST), Govt. of India (Ref. no. EEQ/2018/000963).

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Authors and Affiliations

  1. Department of Electrical Engineering, Jadavpur University, 188, Raja S.C. Mullick Road, Jadavpur, Kolkata, West Bengal, 700032, India

    Hritam Basak & Rohit Kundu

  2. Theism Medical Diagnostics Centre, Dum Dum, Kolkata, West Bengal, 700030, India

    Sukanta Chakraborty

  3. Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mullick Road, Jadavpur, Kolkata, West Bengal, 700032, India

    Nibaran Das

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  1. Hritam Basak
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Correspondence to Nibaran Das.

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This article is part of the topical collection “AI and Deep Learning Trends in Healthcare” guest edited by KC Santosh, Paolo Soda and Zalelam Temesgen.

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Basak, H., Kundu, R., Chakraborty, S. et al. Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection. SN COMPUT. SCI. 2, 369 (2021). https://doi.org/10.1007/s42979-021-00741-2

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