Machine Learning Based Approach for Automated Cervical Dysplasia Detection Using Multi-Resolution Transform Domain Features

Kangkana Bora (), Lipi B. Mahanta, Kasmika Borah, Genevieve Chyrmang, Barun Barua, Saurav Mallik, Himanish Shekhar Das and Zhongming Zhao ()
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Kangkana Bora: Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India
Lipi B. Mahanta: Institute of Advanced Study in Science and Technology, Guwahati 781035, India
Kasmika Borah: Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India
Genevieve Chyrmang: Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India
Barun Barua: Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India
Saurav Mallik: Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
Himanish Shekhar Das: Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India
Zhongming Zhao: Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA

Mathematics, 2022, vol. 10, issue 21, 1-11

Abstract: Pattern detection and classification of cervical cell dysplasia can assist with diagnosis and treatment. This study aims to develop a computational model for real-world applications for cervical dysplasia that has the highest degree of accuracy and the lowest computation time. Initially, an ML framework is created, which has been trained and evaluated to classify dysplasia. Three different color models, three multi-resolution transform-based techniques for feature extraction (each with different filters), two feature representation schemes, and two well-known classification approaches are developed in conjunction to determine the optimal combination of “transform (filter) ⇒ color model ⇒ feature representation ⇒ classifier”. Extensive evaluations of two datasets, one is indigenous (own generated database) and the other is publicly available, demonstrated that the Non-subsampled Contourlet Transform (NSCT) feature-based classification performs well, it reveals that the combination “NSCT (pyrexc,pkva), YCbCr, MLP” gives most satisfactory framework with a classification accuracy of 98.02% (average) using the F1 feature set. Compared to two other approaches, our proposed model yields the most satisfying results, with an accuracy in the range of 98.00–99.50%.

Keywords: cervical dysplasia; pap smear images; discrete wavelet transform; ripplet transform; non sub-sampled; contourlet transform (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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