Citation :

Neny Rosmawarni, Zeratul Izza Mohd Yusoh, Yun Houy Choo, Thoyyibah, Karunia Agustiani, Nadra, "A Comparative Study of Prostate Cancer Classification Using MRI Images with a Machine Learning Approach for Early Diagnosis," International Journal of Engineering Trends and Technology (IJETT), vol. 73, no. 12, pp. 218-228, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I12P118

Abstract

Prostate cancer is one of the most common malignant cancers worldwide. Early detection and diagnosis are essential for treating this Cancer. This study uses features extracted from the Grey Level Co-occurrence Matrix (GLCM) with the Extreme Gradient Boosting (XGBoost) classifier to improve prostate cancer classification using MRI images, with training, validation, and testing. 961 public MRI images consisting of 424 cancerous and 537 non-cancerous images were used. GLCM was used at four angles (0°, 45°, 90°, and 135°) for several texture features: correlation, energy, and homogeneity. The experimental results on the training data achieved an accuracy of 99.8%; on the validation data, the accuracy reached 63.2%; and on the testing data, the precision was 71.6% and the recall was 70.37%. The accuracy results show that the GLCM with the XGBoost model is very effective at capturing discriminative features and achieving balanced classification performance. The proposed model presents a promising foundation for developing automated, data-driven tools in early prostate cancer detection. Future research will focus on hyperparameter tuning, data augmentation, and regularisation to further improve model generalisation and clinical applicability.

Keywords

Prostate cancer, MRI Images, GLCM, XGBoost, Machine Learning, Classification.

References

[1] Sobia Wasim, Sang-Yoon Lee, and Jaehong Kim, “Complexities of Prostate Cancer,” International Journal of Molecular Science, vol. 23, no. 22, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Eka Yudha Rahman et al., “Empowering Health Workers in Early Detection of Prostate Cancer at the North Banjarbaru Community Health Center,” ILUNG Community Service Journal (Superior Wetland Innovation), vol. 3, no. 3, pp. 516-526, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Fari Katul Fikriah, Amelia Devi Putri Ariyanto, and Arif Fitra Setyawan, “Classification of MRI Results of Brain Tumors with GRAY Level Co-Occurance Matrix (GLCM) Feature Extraction,” Rabit Journal of Technology and Information Systems Univrab, vol. 9, no. 2, pp. 343-350, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Rafli Dika Pramudya, “Classification of Brain Tumors based on MRI Images with the Random Forest Classifier Method using GLCM Feature Extraction,” Thesis, Veteran National Development University Jakarta, 2024.
[Google Scholar] [Publisher Link]

[5] Adi Muzakir, Anita Desiani, and Ali Amran, “Classification of Prostate Cancer Disease Using Naïve Bayes and K-Nearest Neighbor Algorithms,” Komputika: Journal of Computer Systems, vol. 12, no. 1, pp. 73-79, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Acep Handika Permana, Fajri Rakhmat Umbara, and Hope Kasyidi, “Classification of Cardiovascular Type Heart Disease using Adaptive Synthetic Sampling and Extreme Gradient Boosting Algorithms,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 1, pp. 499-508, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Kairupan Indah Yessi et al., “An Extreme Gradient Boosting Approach for Classification and Sentiment Analysis,” The Asian Journal of Technology Management, vol. 16, no. 3, pp. 211-225, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Hasria Alang Hafsah, and Muh Sri Yusal, “Increasing Knowledge of ‘Prostate Cancer’ in the Community of Mapung Buttu Hamlet, Campalagian District, Polewali Mandar Regency,” CREATIVE: Indonesian Journal of Community Service, vol. 2, no. 2, pp. 1-6, 2022.
[Google Scholar]

[9] Viva Ratih Bening Ati, “Risk Factors Associated with the Incidence of Prostate Cancer (Case Study at Prof. Dr. Margo Soekarno Purwokerto Regional Hospital),” Mandala of Health, vol. 14, no. 2, pp. 67-73, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Hanisa Hanisa, I Putu Eka Juliantara, and Edwien Setiawan Saputra, “Cervical MRI Examination Management in Cases of Spinal Cord Syringomyelia at Primaya Hospital, Tangerang,” Journal of Research in the Medical Science Cluster, vol. 2, no. 2, pp. 30-40, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Rio Aditya Pahlevi, and Bayu Setiaji, “Analysis of Application Haar Cascade Classifier and Local Binary Pattern Histogram Algorithm in Recognizing Faces with Real-Time Grayscale Images using Opencv,” Journal of Informatics Engineering (Jutif), vol. 4, no. 1, pp. 179-186, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Orry Adrianus Mokola, “Predicting Honey Production Amount Based on Honeycomb Size Using Image Processing Method,” Animal Science Research Journal, vol. 1, no. 2, pp. 31-41, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Hotma Pangaribuan, and Sunarsan Sitohang, “Improving Edge Detection Quality with Image Segmentation Method,” Remik, vol. 7, no. 1, pp. 591-601, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Muhammad Fakhrurrozy Cahyadi, Siswan Syahputra, and Mili Alfhi Syari, “Implementation of Thresholding Method in Digital Image Transformation Process,” Educate Journal of Educational Sciences and Teaching, vol. 1, no. 3, pp. 319-346, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Kartika Candra Kirana et al., “Classification of Brain Tumor Disease using K-Nearest Neighbor based on GLCM,” Tekno Journal of Electrical and Vocational Technology, vol. 33, no. 1, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Epimack Michael et al., “Breast Cancer Segmentation Methods: Current Status and Future Potentials,” BioMed Research International, vol. 2021, no. 1, pp. 1-29, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Desi Nurnaningsih et al., “Identification of Rhizome Type Medicinal Plant Images Using Euclidean Distance Based on Shape and Texture Characteristics,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 3, pp. 171-178, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Adrian Budi Prawira, Jayanta Jayanta, and Yuni Widiastiwi, “Application of the Gray Level Co-Occurance Matrix Method and the Support Vector Machine Algorithm in Classifying Jujube Plants Based on Leaf Texture,” Proceedings of the National Seminar on Computer Science and its Applications, vol. 2, no. 1, pp. 569-578, 2021.
[Google Scholar] [Publisher Link]

[19] Lei Huang et al., “Normalization Techniques in Training DNNs: Methodology, Analysis and Application,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10173-10196, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Henderi Henderi, Tri Wahyuningsih, and Efana Rahwanto, “Comparison of Min-Max Normalization and Z-Score Normalization in the K-Nearest Neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer,” International Journal of Informatics and Information Systems, vol. 4, no. 1, pp. 13-20, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Pattaramon Vuttipittayamongkol, Eyad Elyan, and Andrei Petrovski, “On the Class Overlap Problem in Imbalanced Data Classification,” Knowledge-Based Systems, vol. 212, pp. 1-55, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[22] Wilda Imama Sabilla, and Candra Bella Vista, “Implementation of SMOTE and under Sampling on Imbalanced Datasets for Predicting Company Bankruptcy,” Journal of Applied Computing, vol. 7, no. 2, pp. 329-339, 2021.
[CrossRef] [Google Scholar]  [Publisher Link]

[23] Jan Melvin Ayu Soraya Dachi, and Pardomuan Sitompul, “Comparative Analysis of the XGBoost Algorithm and the Random Forest Ensemble Learning Algorithm on Credit Decision Classification,” Research Journal of Mathematics and Natural Sciences Cluster, vol. 2, no. 2, pp. 87-103, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[24] Joan Y. Chiao, Philosophy of Computational Cultural Neuroscience, Machine Learning, 1^st ed., New York, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Radhika Tibrewala et al., “FastMRI Prostate: A Public, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging,” Scientific Data, vol. 11, no. 1, pp. 1-9, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Pratima Kumari, and Durga Toshniwal, “Extreme Gradient Boosting and Deep Neural Network based Ensemble Learning Approach to Forecast Hourly Solar Irradiance,” Journal of Cleaner Production, vol. 279, 2021.
[CrossRef] [Google Scholar] [Publisher Link]