Open Access

REVIEW

Improvement method for cervical cancer detection: A comparative analysis

NUR AIN ALIAS¹, WAN AZANI MUSTAFA^1,2,*, MOHD AMINUDIN JAMLOS³, AHMED ALKHAYYAT⁴, KHAIRUL SHAKIR AB RAHMAN⁵, RAMI Q. MALIK⁶

1 Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar, Perlis, 02100, Malaysia
2 Advanced Computing (AdvCOMP), Centre of Excellence, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau, Perlis, 02600, Malaysia
3 Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar, Perlis, 02100, Malaysia
4 Department of Computer Technical Engineering, College of Technical Engineering, The Islamic University, Najaf, 54003, Iraq
5 Department of Pathology, Hospital Tuanku Fauziah, Kangar, Perlis, 02000, Malaysia
6 Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Hillah, 51001, Iraq

* Corresponding Author: WAN AZANI MUSTAFA. Email:

Oncology Research 2021, 29(5), 365-376. https://doi.org/10.32604/or.2022.025897

Received 04 August 2022; Accepted 28 September 2022; Issue published 10 October 2022

Abstract

Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab, and the dataset used is established Herlev Dataset. The results show that the highest performance assessment metric values obtain from Method 1: Thresholding and Trace region boundaries in a binary image with the values of precision 1.0, sensitivity 98.77%, specificity 98.76%, accuracy 98.77% and PSNR 25.74% for a single type of cell. Meanwhile, the average values of precision were 0.99, sensitivity 90.71%, specificity 96.55%, accuracy 92.91% and PSNR 16.22%. The experimental results are then compared to the existing methods from previous studies. They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values. On the other hand, the majority of current approaches can be used with either a single or a large number of cervical cancer smear images. This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

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