Title: Classification and comparison of malignancy detection of cervical cells based on nucleus and textural features in microscopic images of uterine cervix

Authors: P.B. Shanthi; Shivani Modi; K.S. Hareesha; Sampath Kumar

Addresses: Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India ' Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India ' Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India ' Department of Electronics and Communication, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India

Abstract: Cervical cancer is one of the major causes of death among women worldwide. Pap smear is the cytology based screening test which is used to detect abnormal cervical cells including pre-cancerous lesions. Accurate classification of Pap smear images is one of the challenging tasks in medical image processing and its performance can be enhanced by extracting and selecting the well-defined features and classifiers. The irregular chromatin structure is one of the prominent diagnostic features of abnormal cells. Classification is performed based on the extracted textural features and the benchmark Herlev dataset features. Radial basis function (RBF), polynomial and sigmoid SVM kernels are used for classification and comparison is performed with the features in benchmark database. Precision, recall and accuracy were calculated for all the combinations of the features. The classifier gives promising results when benchmark features are combined with textural features and also benchmark nucleus features with textural features. An effective integration of features for cervical cell classification had given good results for fast abnormal cell detection and primary Pap smear cell image classification.

Keywords: Pap smear; cervical cancer; textural feature; GLCM; gray level co-occurrence matrix; SVM kernel.

DOI: 10.1504/IJMEI.2021.111861

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.1, pp.1 - 13

Received: 01 Aug 2018
Accepted: 29 Jan 2019
Published online: 18 Dec 2020 *

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