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All Journal Biotika: Jurnal Ilmiah Biologi Microbiology Indonesia International Journal of Integrated Health Sciences Majalah Kedokteran Bandung Jurnal Kesehatan Andalas Padjadjaran Journal of Dentistry Paediatrica Indonesiana Jurnal Ilmiah Kesehatan Anakes: Jurnal Ilmiah Analis Kesehatan Journal Medical Informatics Technology
Prima Nanda Fauziah
Universitas Mohammad Husni Thamrin
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Dentistry Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health

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Journal : Journal Medical Informatics Technology

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Identification of Cell Images in Pap Smear Using GLCM and Classification Methods in Machine Learning Agustino, Rano; Fauziah, Prima Nanda
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.85

Abstract

Early detection of cervical cancer is critical for improving patient outcomes, and accurate classification of Pap smear images supports clinical decision-making. This study aimed to improve cervical cancer diagnosis by classifying Pap smear images using texture features. A dataset of 250 images across five classes underwent preprocessing including grayscale conversion and noise removal. Texture features such as contrast, dissimilarity, homogeneity, energy, correlation, and Angular Second Moment (ASM) were extracted using the Gray-Level Co-occurrence Matrix (GLCM). These features were then used to train and evaluate machine learning algorithms: Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Neural Networks (NN). The Decision Tree model achieved the highest accuracy of 95%, outperforming Neural Networks which reached 74%. Ensemble methods like RF and GB showed robust performance across classes. These results demonstrate the effectiveness of GLCM-based feature extraction combined with Decision Tree classification for accurate and reliable Pap smear image analysis. This approach offers valuable insights for enhancing clinical decision support in cervical cancer diagnosis.
Co-Authors - Chrysanti Aditia, Erie Affiyanti, Wirda Akhmad Yogi Pramatirta, Akhmad Yogi Alislam, Mochamad Arief Ani Melani Maskoen Aruna Gamarani Atna Permana Audia, Desi Basyir, Vaulinne Budi Handono Catu Umirestu Nurdiani Chrysanti Chrysanti Damayanti, Vila Dany Hilmanto Desi Audia Despiagia Pitaloka Dian Firmansyah Dida A. Gurnida DIKI HILMI Dzulfikar Djalil Lukmanul Hakim El Jannah, Syarifah Miftahul Ellis Susanti Erie Aditia Esti Istiqomah Fadil Oenzil Fakhrizal Hariyanto Wahdi Harsono Setiawan Heru Purwanto Nugroho Iis Herawati IIS HERAWATI Imas Latifah Imas Latifah Januar Rhamadani Jeffry Iman Gurnadi Jetty Nurhajati Jetty Nurhajati Johanes C. Mose Johanes C. Mose Latifah, Imas Lenggo Geni Lestari, Estu Mahmudah Mahmudah Masdianto Masdianto Masdianto Masdianto Masdianto Masdianto Masdianto Masdianto, Masdianto Mochamad Arief Alislam Mukti, Septiana Widya Murleni Dorawati Nugroho, Heru Purwanto Nurdiani, Catu Umirestu Permana, Atna Pitaloka, Despiagia Pratiwi, Isnaini Nisa Restu Rano Agustino Ratna Mutu Manikam Retno Martini Widhyasih Septiana Widya Mukti Subastiono, Aditia Suhara, Intan Ryatni Syahrial Harun Syarifah Miftahul El Jannah Vaulinne Basyir Vira Amalia Wahdi, Fakhrizal Hariyanto Widhyasih, Retno Martini Widiarsih, Erlina Widiarsih, Erlina Wirda Affiyanti Yanwirasti Yanwirasti Yanwirasti Yanwirasti Yuliati, Tri Yuliati, Tri Yunia Qonitatin Al Masyani Zaranggi, Ahmad Zuraida Zuraida ZURAIDA ZURAIDA