Abstract
Prostate cancer diagnosis utilizes Gleason grading to analyze biopsy images to establish cancer severity levels. The analysis of prostate biopsy images is an important step in automating the Gleason grading system, which helps in prostate cancer diagnosis and prognosis. The subjective evaluation of manual grading methods exposes vulnerabilities since they lead to inconsistent results so automated solutions have become essential for precision and reliability. Present machine learning algorithms show insufficient robustness because they incorporate inadequate feature extraction approaches together with inadequate classifier choices. An ensemble Extra Trees model with characteristics from prostate biopsy images serves as the proposal for Gleason grading classification. The HSV color space produces three statistics (Mean, Standard Deviation, and Skewness) from colors with addition of entropy alongside four texture features derived from GLCM analysis which includes Contrast, Energy, Homogeneity, and Correlation. The proposed model receives evaluation against several classifiers which include Nearest Neighbors, Linear SVM, Decision Tree, and Random Forest. The ensemble Extra Trees classifier reaches 99% accuracy during testing which proves better than baseline models thus indicating its potential in trustworthy prostate cancer grading. The significance of this research is to improve the accuracy and efficiency of Gleason grading in prostate biopsy images using machine learning, aiding in early diagnosis and better treatment planning for prostate cancer.
References
Arita, Yuki, Christian Roest, Thomas C Kwee, Ramesh Paudyal, Alfonso Lema-Dopico, Stefan Fransen, Daisuke Hirahara, Eichi Takaya, Ryo Ueda, and Lisa Ruby. “Advancements in Artificial Intelligence for Prostate Cancer: Optimizing Diagnosis, Treatment, and Prognostic Assessment.” Asian Journal of Urology, 2025.
Badashah, Syed Jahangir, Afaque Alam, Malik Jawarneh, Tejashree Tejpal Moharekar, Venkatesan Hariram, Galiveeti Poornima, and Ashish Jain. “Cancer Classification and Detection Using Machine Learning Techniques.” Natural Language Processing for Software Engineering, 2025, 95–111. https://doi.org/10.1002/9781394272464.ch6.
Chen, Fuyao, Roxana Esmaili, Ghazal Khajir, Tal Zeevi, Moritz Gross, Michael Leapman, Preston Sprenkle, Amy C Justice, Sandeep Arora, and Jeffrey C Weinreb. “Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate Biopsies.” European Urology Oncology, 2025.
Esteban, Luis Mariano, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, and Juan Morote. “Integrating Radiological and Clinical Data for Clinically Significant Prostate Cancer Detection with Machine Learning Techniques.” Scientific Reports 15, no. 1 (2025): 4261. https://doi.org/10.1038/s41598-025-88297-6.
Antunes, Maria Eliza, Thaise Gonçalves Araújo, Tatiana Martins Till, Eliana Pantaleão, Paulo F.A. Mancera, and Marta Helena de Oliveira. “Machine Learning Models for Predicting Prostate Cancer Recurrence and Identifying Potential Molecular Biomarkers.” Frontiers in Oncology 15 (2025): 1535091. https://doi.org/10.3389/fonc.2025.1535091.
Stojadinovic, Miroslav, Milorad Stojadinovic, and Slobodan Jankovic. “Predicting Intermediate-Risk Prostate Cancer Using Machine Learning.” International Urology and Nephrology, 2025, 1–10. https://doi.org/10.1007/s11255-024-04342-9.
Arafa, Mostafa A, Karim H Farhat, Sherin F Aly, Farrukh K Khan, Alaa Mokhtar, Abdulaziz M Althunayan, Waleed Al-Taweel, Sultan S Al-Khateeb, Sami Azhari, and Danny M Rabah. “Prediction of Prostate Biopsy Outcomes at Different Cut-Offs of Prostate-Specific Antigen Using Machine Learning: A Multicenter Study.” Journal of the Egyptian National Cancer Institute 37, no. 1 (2025): 8.
Vasconcelos Ordones, Flavio, Paulo Roberto Kawano, Lodewikus Vermeulen, Ali Hooshyari, David Scholtz, Peter John Gilling, Darren Foreman, et al. “A Novel Machine Learning-Based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator.” Urology 196 (2024): 20–26. https://doi.org/10.1016/j.urology.2024.11.001.
Yennapusa, Haritha, Rakesh Ramakrishnan, Balakumar Muniandi, Ketan Gupta, and J. Logeshwaran. “An Enhanced Optimization of Machine Learning Model in Prostate Cancer Detection.” In Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024, 5:184–90. IEEE, 2024. https://doi.org/10.1109/IC2PCT60090.2024.10486449.
Guerra, Adalgisa, Matthew R. Orton, Helen Wang, Marianna Konidari, Kris Maes, Nickolas K. Papanikolaou, and Dow Mu Koh. “Clinical Application of Machine Learning Models in Patients with Prostate Cancer before Prostatectomy.” Cancer Imaging 24, no. 1 (2024): 24. https://doi.org/10.1186/s40644-024-00666-y.
Rippa, Malte, Ruben Schulze, Georgia Kenyon, Marian Himstedt, Maciej Kwiatkowski, Rainer Grobholz, Stephen Wyler, Alexander Cornelius, Sebastian Schindera, and Felice Burn. “Evaluation of Machine Learning Classification Models for False-Positive Reduction in Prostate Cancer Detection Using MRI Data.” Diagnostics 14, no. 15 (2024): 1677. https://doi.org/10.3390/diagnostics14151677.
Morakis, Dimitrios, and Adam Adamopoulos. “Hybrid Machine Learning Algorithms to Evaluate Prostate Cancer.” Algorithms 17, no. 6 (2024): 236. https://doi.org/10.3390/a17060236.
Wang, Ya Xuan, Li Ma, Jiaxin He, Hai Juan Gu, and Hai Xia Zhu. “Identification of Cancer Stem Cell-Related Genes through Single Cells and Machine Learning for Predicting Prostate Cancer Prognosis and Immunotherapy.” Frontiers in Immunology 15 (2024): 1464698. https://doi.org/10.3389/fimmu.2024.1464698.
Wang, Ya Xuan, Bo Ji, Lu Zhang, Jinfeng Wang, Jia Xin He, Bei Chen Ding, and Ming Hua Ren. “Identification of Metastasis-Related Genes for Predicting Prostate Cancer Diagnosis, Metastasis and Immunotherapy Drug Candidates Using Machine Learning Approaches.” Biology Direct 19, no. 1 (2024): 50. https://doi.org/10.1186/s13062-024-00494-x.
Vizza, Patrizia, Federica Aracri, Pietro Hiram Guzzi, Marco Gaspari, Pierangelo Veltri, and Giuseppe Tradigo. “Machine Learning Pipeline to Analyze Clinical and Proteomics Data: Experiences on a Prostate Cancer Case.” BMC Medical Informatics and Decision Making 24, no. 1 (2024): 93. https://doi.org/10.1186/s12911-024-02491-6.
Cheng, Guoliang, Junrong Xu, Honghua Wang, Jingzhao Chen, Liwei Huang, Zhi Rong Qian, and Yong Fan. “MtPCDI: A Machine Learning-Based Prognostic Model for Prostate Cancer Recurrence.” Frontiers in Genetics 15 (2024): 1430565. https://doi.org/10.3389/fgene.2024.1430565.
Tolkach, Yuri, Vlado Ovtcharov, Alexey Pryalukhin, Marie Lisa Eich, Nadine Therese Gaisa, Martin Braun, Abdukhamid Radzhabov, et al. “An International Multi-Institutional Validation Study of the Algorithm for Prostate Cancer Detection and Gleason Grading.” Npj Precision Oncology 7, no. 1 (2023): 77. https://doi.org/10.1038/s41698-023-00424-6.
Xiang, Jinxi, Xiyue Wang, Xinran Wang, Jun Zhang, Sen Yang, Wei Yang, Xiao Han, and Yueping Liu. “Automatic Diagnosis and Grading of Prostate Cancer with Weakly Supervised Learning on Whole Slide Images.” Computers in Biology and Medicine 152 (2023): 106340. https://doi.org/10.1016/j.compbiomed.2022.106340.
Nai, Ying Hwey, Dennis Lai Hong Cheong, Sharmili Roy, Trina Kok, Mary C. Stephenson, Josh Schaefferkoetter, John J. Totman, et al. “Comparison of Quantitative Parameters and Radiomic Features as Inputs into Machine Learning Models to Predict the Gleason Score of Prostate Cancer Lesions.” Magnetic Resonance Imaging 100 (2023): 64–72. https://doi.org/10.1016/j.mri.2023.03.009.
Nematollahi, Hamide, Masoud Moslehi, Fahimeh Aminolroayaei, Maryam Maleki, and Daryoush Shahbazi-Gahrouei. “Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods.” Diagnostics 13, no. 4 (2023): 806. https://doi.org/10.3390/diagnostics13040806.
Jatain, Aman, Manju, and Priyanka Vashisht. “Gleason Grading System for Prostate Cancer Diagnosis.” In Artificial Intelligence-Based Healthcare Systems, 195–207. Springer, 2023. https://doi.org/10.1007/978-3-031-41925-6_14.
Chen, Gang, Xuchao Dai, Mengqi Zhang, Zhujun Tian, Xueke Jin, Kun Mei, Hong Huang, and Zhigang Wu. “Machine Learning-Based Prediction Model and Visual Interpretation for Prostate Cancer.” BMC Urology 23, no. 1 (2023): 164. https://doi.org/10.1186/s12894-023-01316-4.
Gomes, Egleidson F.A., Eduardo Paulino Junior, Mário F.R. de Lima, Luana A. Reis, Giovanna Paranhos, Marcelo Mamede, Francis G.J. Longford, Jeremy G. Frey, and Ana Maria de Paula. “Prostate Cancer Tissue Classification by Multiphoton Imaging, Automated Image Analysis and Machine Learning.” Journal of Biophotonics 16, no. 6 (2023): e202200382. https://doi.org/10.1002/jbio.202200382.
Prata, Francesco, Umberto Anceschi, Ermanno Cordelli, Eliodoro Faiella, Angelo Civitella, Piergiorgio Tuzzolo, Andrea Iannuzzi, et al. “Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features.” Current Oncology 30, no. 2 (2023): 2021–31. https://doi.org/10.3390/curroncol30020157.
Alataş, Emre, Handan Tanyıldızı Kökkülünk, Hilal Tanyıldızı, and Goksel Alcın. “Treatment Prediction with Machine Learning in Prostate Cancer Patients.” Computer Methods in Biomechanics and Biomedical Engineering, 2023, 1–9. https://doi.org/10.1080/10255842.2023.2298364.
Xie, H. (2020). PANDA - Resized Train Data (512x512)[Dataset]. Kaggle. https://www.kaggle.com/datasets/xhlulu/panda-resized-train-data-512x512