A State-of-the-Art Review on Machine Learning-based Methods for Prostate Cancer Diagnosis

  • Ari Mohammed ali Ahmed Department of Information Technology, Technical College of Informatics, Sulaimani Polytechnic University, KRG, Sulaimani, Iraq,
  • Aree Ali Mohammed, Professor Department of Computer Science, College of Science, University of Sulaimani, Sulaymaniyah, Iraq


Prostate cancer can be viewed as the second most dangerous and diagnosed cancer of men all over the world. In the past decade, machine and deep learning methods play a significant role in improving the accuracy of classification for both binary and multi classifications. This review is aimed at providing a comprehensive survey of the state of the art in the past 5 years from 2015 to 2020, focusing on different datasets and machine learning techniques. Moreover, a comparison between studies and a discussion about the potential future researches is described. First, an investigation about the datasets used by the researchers and the number of samples associated with each patient is performed. Then, the accurate detection of each research study based on various machine learning methods is given. Finally, an evaluation of five techniques based on the receiver operating characteristic curve has been presented to show the accuracy of the best technique according to the area under curve (AUC) value. Conducted results indicate that the inception-v3 classifier has the highest score for AUC, which is 0.91.


[1] Lemaître, R. Martí, J. Freixenet, J. C. Vilanova, P. M. Walker and F. Meriaudeau. “Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review”. Computers in Biology and Medicine, vol. 60, pp. 8-31, 2015.
[2] T. Saba. “Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges”. Journal of Infection and Public Health, vol. 13, no. 9, pp. 1274-1289, 2020.
[3] S. Bhattacharjee, H. G. Park, C. H. Kim, D. Prakash, N. Madusanka, J. H. So, N. H. Cho and H. K. Choi. “Quantitative analysis of benign and malignant tumors in histopathology: Predicting prostate cancer grading using SVM”. Applied Sciences, vol. 9, no. 15, 2019.
[4] S. Liu, H. Zheng, Y. Feng and W. Li. “Prostate cancer diagnosis using deep learning with 3d multiparametric MRI”. SPIE Proceedings, vol. 10134, pp. 3-6, 2017.
[5] N. Aldoj, S. Lukas, M. Dewey and T. Penzkofer. “Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network”. European Radiology, vol. 30, no. 2, pp. 1243-1253, 2020.
[6] B. Abraham and M. S. Nair. “Automated grading of prostate cancer using convolutional neural network and ordinal class classifier”. Informatics in Medicine Unlocked, vol. 17, p. 100256, 2019.
[7] L. A. Torre, B. Trabert, C. E. DeSantis, K. D. Miller, G. Samimi, C. D. Runowicz, M. M. Gaudet, A. Jemal, R. L. Siegel. “Ovarian cancer statistics, 2018”. CA: A Cancer Journal for Clinicians, vol. 68, no. 4, pp. 284-296, 2018.
[8] M. Arif, I. G. Schoots, J. C. Tovar, C. H. Bangma, G. P. Krestin, M. J. Roobol, W. Niessen and J. F. Veenland. “Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI”. European Radiology, vol. 30, pp. 6582-6592, 2020.
[9] L. Brunese, F. Mercaldo, A. Reginelli and A. Santone. “Formal methods for prostate cancer Gleason score and treatment prediction using radiomic biomarkers”. Magnetic Resonance Imaging, vol. 66, pp. 165-175, 2020.
[10] R. Sammouda, H. Aboalsamh and F. Saeed. “Comparison Between K Mean and fuzzy C-mean Methods for Segmentation of Near Infrared Fluorescent Image for Diagnosing Prostate Cancer”. International Conference on Computer Vision and Image Analysis Applications, 2015.
[11] P. Mohapatra and S. Chakravarty. “Modified PSO Based Feature Selection for Microarray Data Classification”. 2015 IEEE Power, Communication and Information Technology Conference, pp. 703- 709, 2015.
[12] S. H. Bouazza, N. Hamdi, A. Zeroual and K. Auhmani. “Geneexpression-based Cancer Classification through Feature Selection with KNN and SVM Classifiers”. 2015 Intelligent Systems and Computer Vision, 2015.
[13] P. Mohapatra, S. Chakravarty and P. K. Dash. “Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system”. Swarm and Evolutionary Computation, vol. 28, pp. 144-160, 2016.
[14] F. Imani, S. Ghavidel, P. Abolmaesumi, S. Khallaghi, E. Gibson, A. Khojaste, M. Gaed, M. Moussa, J. A. Gomez, C. Romagnoli, D. W. Cool, M. Bastian-Jordan, Z. Kassam, D. R. Siemens, M. Leveridge, S. Chang, A. Fenster, A. D. Ward and P. Mousavi. “Fusion of Multi-parametric MRI and Temporal Ultrasound for Characterization of Prostate Cancer: In vivo Feasibility Study”. Medical Imaging 2016: Computer-Aided Diagnosis, vol. 9785, p. 97851K, 2016.
[15] M. Ram, A. Najafi and M. T. Shakeri. “Classification and biomarker genes selection for cancer gene expression data using random forest”. The Iranian Journal of Pathology, vol. 12, no. 4, pp. 339-347, 2017.
[16] Y. Sun, H. Reynolds, D. Wraith, S. Williams, M. E. Finnegan, C. Mitchell, D. Murphy, M. A. Ebert and A. Haworth. “Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: A preliminary study”. Physical and Engineering Sciences in Medicine, vol. 40, no. 1, pp. 39-49, 2017.
[17] Y. Liu and X. An. “A Classification Model for the Prostate Cancer Based on Deep Learning,” Proceedings of the 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017, pp. 1-6, 2018.
[18] I. Reda, A. Shalaby, M. Elmogy, A. A. Elfotouh, F. Kahalifa, M. A. El-Ghar, E. Hosseini-Asl, G. Gimel’farb, N. Werghi and A. El-Baz. “A New CNN-Based System for Early Diagnosis of Prostate Cancer”. Proceedings International Symposium on Biomedical Imaging, pp. 207-210, 2018.
[19] S. Yoo, I. Gujrathi, M. A. Haider and F. Khalvati. “Prostate cancer detection using deep convolutional neural networks”. Scientific Reports, vol. 9, no. 1, pp. 1-10, 2019.
[20] K. Cahyaningrum, Adiwijaya and W. Astuti. “Microarray Gene Expression Classification for Cancer Detection using Artificial Neural Networks and Genetic Algorithm Hybrid Intelligence,” 2020 International Conference on Data Science and its Applications, 2020.
[21] L. Duran-Lopez, J. P. Dominguez-Morales, A. F. Conde-Martin, S. Vicente-Diaz and A. Linares-Barranco. “PROMETEO: A CNNbased computer-aided diagnosis system for WSI prostate cancer detection”. IEEE Access, vol. 8, pp. 128613-128628, 2020.
[22] . Liu, C. Yang, J. Huang, S. Liu, Y. Zhuo and X. Lu. “Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer”. Future Generation Computer Systems, vol. 114, pp. 358-367, 2021.
[23] A. Z. Shirazi, S. J. S. Mahdavi Chabok and Z. Mohammadi. “A novel and reliable computational intelligence system for breast cancer detection”. Medical and Biological Engineering and Computing, vol. 56, no. 5, pp. 721-732, 2018.
[24] M. Nour, Z. Cömert and K. Polat. “A novel medical diagnosis model for COVID-19 infection detection based on deep features and bayesian optimization”. Applied Soft Computing, vol. 97, pp. 1-13, 2020.
[25] Y. Celik, M. Talo, O. Yildirim, M. Karabatak and U. R. Acharya. “Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images”. Pattern Recognition Letters, vol. 133, pp. 232-239, 2020.
How to Cite
AHMED, Ari Mohammed ali; MOHAMMED, Aree Ali. A State-of-the-Art Review on Machine Learning-based Methods for Prostate Cancer Diagnosis. UHD Journal of Science and Technology, [S.l.], v. 5, n. 1, p. 41-47, mar. 2021. ISSN 2521-4217. Available at: <https://journals.uhd.edu.iq/index.php/uhdjst/article/view/792>. Date accessed: 13 may 2021. doi: https://doi.org/10.21928/uhdjst.v5n2y2021.pp41-47.