Classification of adeno carcinoma, high squamous intraephithelial lesion, and squamous cell carcinoma in Pap smear images based on extreme learning machine

Suksmono, Andriyan Bayu and Rulaningtyas, Riries and Triyana, Kuwat and Sitanggang, Imas Sukaesih and Rahaju, Anny Setijo and Kusumastuti, Etty Hary and Nabila, Ahda Nur Laila and Maharani, Rizkya Nabila and Ismayanto, Difa Fanani and Katherine, Katherine and Winarno, Winarno and Putra, Alfian Pramudita (2021) Classification of adeno carcinoma, high squamous intraephithelial lesion, and squamous cell carcinoma in Pap smear images based on extreme learning machine. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 9 (2). 115 – 120. ISSN 21681163

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Abstract

Cervical cancer is a malignant tumour that attacks the female genital area originating from epithelial metaplasia in the squamous protocol junction area. One method of diagnosis of cervical cancer is to do a Pap smear examination by taking a cervical cell smear from the woman’s cervix and observing its cell development. However, examination of cervical cancer from Pap smear results usually takes a long time. This is because medical practitioners still rely on visual observations in the analysis of the results of Pap smear so that the results are subjective. Therefore, we need a programme that can help the classification process in establishing a diagnosis of cervical cancer with high accuracy results. In this study, a cervical cancer classification program was developed using a combination of the Grey Level Co-occurrence Matrix (GLCM) and Extreme Learning Machine (ELM) methods. There are three classes of cervical cell images classified, namely adenocarcinoma, High Squamous Intraepithelial Lesion (HSIL) and Squamous Cell Carcinoma (SCC). From the results of the training program obtained an accuracy 100 and from the testing program obtained an accuracy of 80. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Item Type: Article
Additional Information: Cited by: 4; All Open Access, Green Open Access
Uncontrolled Keywords: Cells; Computer aided diagnosis; Cytology; Diseases; Image classification; Machine learning; Software testing; Cervical cancers; Cervical cells; Gray-level co-occurrence matrix; Grey-level co-occurrence matrixes; Image-based; Junction area; Malignant tumors; Pap smear; Pap smear images; Squamous cell carcinoma; accuracy; adenocarcinoma; Article; cancer classification; energy; entropy; extreme learning machine; human; learning; learning algorithm; mathematical model; Papanicolaou test; physician; squamous cell carcinoma; squamous cell lesion; training; uterine cervix cancer; Knowledge acquisition
Subjects: Q Science > QC Physics
Divisions: Faculty of Mathematics and Natural Sciences > Physics Department
Depositing User: Sri JUNANDI
Date Deposited: 05 Oct 2024 05:45
Last Modified: 05 Oct 2024 05:45
URI: https://ir.lib.ugm.ac.id/id/eprint/8836

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