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Cervical Cancer Detection Based on Novel Decision Tree Approach

S. R. Sylaja Vallee Narayan1,*, R. Jemila Rose2

1 Department of Computer Science and Engineering, Infant Jesus College of Engineering, Thoothukkudi, Tamilnadu, 628851, India
2 Department of Computer Science and Engineering, St. Xavier’s Catholic College of Engineering, Nagercoil, Tamilnadu, 629003, India

* Corresponding Author: S. R. Sylaja Vallee Narayan. Email: email

Computer Systems Science and Engineering 2023, 44(2), 1025-1038.


Cervical cancer is a disease that develops in the cervix’s tissue. Cervical cancer mortality is being reduced due to the growth of screening programmers. Cervical cancer screening is a big issue because the majority of cervical cancer screening treatments are invasive. Hence, there is apprehension about standard screening procedures, as well as the time it takes to learn the results. There are different methods for detecting problems in the cervix using Pap (Papanicolaou-stained) test, colposcopy, Computed Tomography (CT), Magnetic Resonance Image (MRI) and ultrasound. To obtain a clear sketch of the infected regions, using a decision tree approach, the captured image has to be segmented and analyzed. The goal of creating a decision tree is to establish prediction model that anticipate the feature vector based on the input variable. This paper deals with investigating various techniques of segmentation for detecting the cervical cancer. It proposes a novel method to develop an assistance system for the detection diagnosis of cervical cancer, based on work of Martin, Byriel and Norup. The analysis is focused on Pap smear pictures of single cells. Smear testing is a method of detecting abnormalities in the blood. Image processing is an effective method for extracting data. It is used to determine the size of cervical carcinoma and the length of the uterus. Martin’s database, which is open source and utilised for analysis and validation, is obtainable for research purposes. Cervical malignancy information utilizing three grouping strategies to anticipate the disease and afterward analyzed the outcomes showed that choice tree is the best classifier indicator with the test dataset. Further investigations ought to be led to improve execution.


Cite This Article

APA Style
Narayan, S.R.S.V., Rose, R.J. (2023). Cervical cancer detection based on novel decision tree approach. Computer Systems Science and Engineering, 44(2), 1025-1038.
Vancouver Style
Narayan SRSV, Rose RJ. Cervical cancer detection based on novel decision tree approach. Comput Syst Sci Eng. 2023;44(2):1025-1038
IEEE Style
S.R.S.V. Narayan and R.J. Rose, "Cervical Cancer Detection Based on Novel Decision Tree Approach," Comput. Syst. Sci. Eng., vol. 44, no. 2, pp. 1025-1038. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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