Feature Analysis of Normal Epithelial Cervical Cell Characteristics in Pap Smear Images

Kurniawan, Rahadian; Fudholi, Dhomas Hatta; Muhimmah, Izzati; Kurniawardhani, Arrie; Indrayanti, Indrayanti

doi:10.1109/icicos.2018.8621670

Cited by 1 publication

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“…Pemeriksaan Pap smear dapat diketahui seorang wanita mengidap penyakit kanker serviks atau tidak. Pap smear dilakukan oleh ahli patologi sesuai dengan keahliannya di bawah pengamatan mikroskop [8], namun masih terdapat kesulitan dalam pengamatan sel [9].…”

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Identifikasi Citra Pap Smear RepoMedUNM dengan Menggunakan K-Means Clustering dan GLCM

Riana

Rahayu

Hadianti

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Cervical cancer’s a gynecological malignancy in women that’s very dangerous, even causes death. Prevention through early detection of Pap smear test. It was carried out by pathologists with the help of a microscope still have obstacles in observations. There’re many studies on Pap smear image processing for helping pathologists in cell identification. Availability of Pap smear image dataset is needed in cervical cancer early detection research. The purpose of this study was to segment, feature extraction and classify 180 Pap smear images of RepoMedUNM. The method used to identify Pap smear images begins with preprocessing, namely changing the color in the image to L*a*b color, segmentation using the K-means method, extraction of 6 features, namely metric, eccentricity, contrast, correlation, energy, and homogeneity, and then identified by calculating the closest distance between the training data features and the test data features with the Euclidean distance. The result of identification ThinPrep Pap smear images in 3 classes achieve average accuracy of 93.33%, Non-ThinPrep Pap smear images in 2 classes achieve 90% average accuracy and the average accuracy of the overall in the 4 classes reached 92%. These results indicate that the proposed method can identify Pap smear images well.

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