Open Access
REVIEW
A Narrative Review: Classification of Pap Smear Cell Image for Cervical Cancer Diagnosis
Wan Azani Mustafa1,*, Afiqah Halim1, Khairul Shakir Ab Rahman2
1 Faculty of Engineering Technology, University of Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia
2 Department of Pathology, Hospital Tuanku Fauziah, 02000 Kangar, Perlis, Malaysia
* Corresponding Author: Wan Azani Mustafa. Email:
Oncologie 2020, 22(2), 53-63. https://doi.org/10.32604/oncologie.2020.013660
Abstract
Cervical cancer develops as cells transformation in the cervix of a
female that connects the uterus to the vagina. This cancer may impact the
columnal epithelial cells of the cervix and therefore can be expanded to the
lymphatic and circulatory system (metastasize), sometimes the kidneys, liver,
prostate, vagina, and rectum. Many of the cervical cancer patients survived by
taking early prevention by undergoing a Pap Smear Test. However, the result of
the test usually takes a few weeks which is extremely time-consuming especially
at the government hospital. The purpose of this research was to study the
detection and classification method of the Pap Smear image to resolve the timeconsuming issues and support better system performance to prevent low
precision result of the Human Papilloma Virus (HPV) stages. A few studies were
considered which features the cell image databases to classify cervical cancer
according to its type. Besides, the classification system and the performance of
the preceding papers that had been considered include a few features found in the
cell images. Those features were the size of the cells, the shape of the cells, the
colour, Region of Interest (ROI) and overlapped cell nuclei. The other existing
design methods being considered were the Deep Convolutional Neural Network
(CNN) and the Artificial Neural Network (ANN). These findings technique
showed the highest percentage of the system accuracy, precision, and specificity
that might be excellent for further analysis. The research limitation was the
method of how the numerous image databases needed to be processed and
classified one at a time. None of these articles stated whether they had found the
way to compute more images at once. The aim of the study was to review the
previous paper in order to define the feature datasets that needed to be considered.
The features were important in designing a new classification method and
increasing the performance of the systems. The features included the nucleus
shape, diameter and surface areas, colour and luminosity of the cell datasets, the
region of the nucleus, design and image resolution. In this paper, an extensive
analysis was studied for cervical cancer classification techniques. As expected
from the outcome, the study of the feature database, the classification method
and the system performance were reviewed deeper for further assessments.
Keywords
Cite This Article
Mustafa, W. A., Halim, A., Shakir, K. (2020). A Narrative Review: Classification of Pap Smear Cell Image for Cervical Cancer Diagnosis.
Oncologie, 22(2), 53–63.
Citations