The detection of cervical cancer disease using an adaptive thresholding method through digital image processing

Eggi I. Putri, Rita Magdalena and Ledya Novamizanti
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Eggi I. Putri: Telkom Engineering School (TES)Telkom University (Tel-U), Bandung, Jawa Barat 40257, Indonesia
Rita Magdalena: Telkom Engineering School (TES)Telkom University (Tel-U), Bandung, Jawa Barat 40257, Indonesia
Ledya Novamizanti: Telkom Engineering School (TES)Telkom University (Tel-U), Bandung, Jawa Barat 40257, Indonesia

Journal of Advances in Health and Medical Sciences, 2015, vol. 1, issue 1, 30-36

Abstract: Cervical cancer is a kind of cancer disease caused by human papilloma virus type 16 and 18, attacking woman cervix. To detect a cervical cancer, the frequently-used method is Pap-Smear; however, errors often occur when the method is taken to diagnose the level of cervical cancer. Thus, a proper system is required, which is supposed for being able to help identifying the result of Pap-Smear. This study aims at designing a system to detect the symptoms of cervical cancer using MATLAB to solve these errors. The image processing begins with converting the type of an image, which is followed by a thresholding, and a noise removal using filters until the image has become ready to be detected. For a thresholding process, an Adaptive Thresholding method is taken, in which the thresholding focuses on local threshold values. The system is considerably able to classify images into two types, i.e. normal and abnormal (precancerous). Abnormal type is divided into three subtypes, i.e. mild, moderate and severe. An experiment is conducted on the proposed system, in which it is supposed to analyze 500 test images, including 250 for training and 250 for testing. Based on the testing process, a perfect 100% accuracy rate is obtained, while the average processing time is 25.4 seconds with a WS value at 10 and C value at -2.

Keywords: Cervical cancer Pap smear; Digital image processing; Thresholding (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:apb:jahmss:2015:p:30-36

DOI: 10.20474/jahms-1.1.4

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