Intelligent diagnostic systems significantly enhance the effectiveness and efficiency of cancer detection and management, ultimately leading to better patient outcomes. According to statistics, cancer is the second prime cause of death in males. It's a sluggish-growing ailment that doesn't show symptoms until it's quite evolved. Various investigations on AI (Artificial Intelligence) algorithms analysis have been done in the previous few years over varied medical imaging modalities which includes Computed Tomography, Magnetic Resonance Imaging, and Ultrasound. The use of artificial intelligence to monitor prostate cancer would have a tremendous impact on healthcare. Cancer scientists would have a superior understanding of the ailment and it would be helpful in developing a more precise mechanism for cancer detection as it is the need of the hour, as it has been predicted that there will be over 1.3 million additional cases diagnosed annually around the world. Here an attempt has been made to provide an analysis of the progress being made in the sector of medical image processing. Also, based on the rising interest in CNN (Convolutional Neural Networks) in recent years, we have examined the use of CNN in numerous automatic processing tasks for prostate cancer identification and diagnosis. In this study, a novel deep learning convolutional neural network (CNN) model was employed and its performance was compared against three established CNN models: AlexNet, GoogleNet, and ResNet. It has been found that the use of CNN has increased dramatically, with excellent outputs gained using either new models or pre-conditioned networks for transfer learning. Deep learning-based research surpasses traditional patient prognostic methods with regard to accuracy, according to the survey's findings.
Keywords
Cancer, Artificial Intelligence, Convolutional Neural Network, Deep Learning, Magnetic Resonance Imaging.
S. P. R. Chandrasekhara, M. G. Kabadi, and S. Srivinay, “Wearable IoT based diagnosis of prostate cancer using GLCM-multiclass SVM and SIFT-multiclass SVM feature extraction strategies,” International Journal of Pervasive Computing and Communications, vol. 20, no. 1, pp. 19–37, Sep. 2021, doi: 10.1108/ijpcc-07-2021-0167.
S. P. R. Chandrasekhara, M. G. Kabadi, and Srivinay, “A Novel SIFT-SVM Approach for Prostate Cancer Detection,” Journal of Computer Science, vol. 16, no. 12, pp. 1742–1752, Dec. 2020, doi: 10.3844/jcssp.2020.1742.1752.
Y. Bengio, Y. Lecun, and G. Hinton, “Deep learning for AI,” Communications of the ACM, vol. 64, no. 7, pp. 58–65, Jun. 2021, doi: 10.1145/3448250.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2015, doi: 10.1109/cvpr.2015.7298965.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, doi: 10.1109/cvpr.2016.90.
A. Mehrtash et al., “Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks,” Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134, p. 101342A, Mar. 2017, doi: 10.1117/12.2277123.
S. Wang, K. Burtt, B. Turkbey, P. Choyke, and R. M. Summers, “Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research,” BioMed Research International, vol. 2014, pp. 1–11, 2014, doi: 10.1155/2014/789561.
M. H. Le et al., “Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks,” Physics in Medicine & Biology, vol. 62, no. 16, pp. 6497–6514, Jul. 2017, doi: 10.1088/1361-6560/aa7731.
Sergey Ioffe and Christian Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” Proceedings of the 32 nd International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP Vol 37, pp. 448– 456.
G. J. S. Litjens, P. C. Vos, J. O. Barentsz, N. Karssemeijer, and H. J. Huisman, “Automatic computer aided detection of abnormalities in multi-parametric prostate MRI,” Medical Imaging 2011: Computer-Aided Diagnosis, vol. 7963, p. 79630T, Mar. 2011, doi: 10.1117/12.877844.
S. Liu, H. Zheng, Y. Feng, and W. Li, “Prostate cancer diagnosis using deep learning with 3D multiparametric MRI,” Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134, p. 1013428, Mar. 2017, doi: 10.1117/12.2277121.
E. Arvaniti et al., “Automated Gleason grading of prostate cancer tissue microarrays via deep learning,” Scientific Reports, vol. 8, no. 1, Aug. 2018, doi: 10.1038/s41598-018-30535-1.
Z. Hu, J. Tang, Z. Wang, K. Zhang, L. Zhang, and Q. Sun, “Deep learning for image-based cancer detection and diagnosis − A survey,” Pattern Recognition, vol. 83, pp. 134–149, Nov. 2018, doi: 10.1016/j.patcog.2018.05.014.
K. Nagpal et al., “Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer,” npj Digital Medicine, vol. 2, no. 1, Jun. 2019, doi: 10.1038/s41746-019-0112-2.
S. L. Goldenberg, G. Nir, and S. E. Salcudean, “A new era: artificial intelligence and machine learning in prostate cancer,” Nature Reviews Urology, vol. 16, no. 7, pp. 391–403, May 2019, doi: 10.1038/s41585-019-0193-3.
D. Nguyen et al., “A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning,” Scientific Reports, vol. 9, no. 1, Jan. 2019, doi: 10.1038/s41598-018-37741-x.
W. Bulten et al., “Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study,” The Lancet Oncology, vol. 21, no. 2, pp. 233–241, Feb. 2020, doi: 10.1016/s1470-2045(19)30739-9.
P. Ström et al., “Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study,” The Lancet Oncology, vol. 21, no. 2, pp. 222–232, Feb. 2020, doi: 10.1016/s1470-2045(19)30738-7.
S. G. Armato, N. A. Petrick, and K. Drukker, “PROSTATEx: Prostate MR Classification Challenge (Conference Presentation),” Medical Imaging 2017: Computer-Aided Diagnosis, p. 274, Apr. 2017, doi: 10.1117/12.2280374.
J. Glaister, A. Cameron, A. Wong, and M. A. Haider, “Quantitative investigative analysis of tumour separability in the prostate gland using ultra-high b-value computed diffusion imaging,” 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 420–423, Aug. 2012, doi: 10.1109/embc.2012.6345957.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” OSDI'16: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, 2016, pp. 265-283.
A. Gertych et al., “Machine learning approaches to analyze histological images of tissues from radical prostatectomies,” Computerized Medical Imaging and Graphics, vol. 46, pp. 197–208, Dec. 2015, doi: 10.1016/j.compmedimag.2015.08.002.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, doi: 10.1109/cvpr.2017.243.
W. Li et al., “Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images,” IEEE Transactions on Medical Imaging, vol. 38, no. 4, pp. 945–954, Apr. 2019, doi: 10.1109/tmi.2018.2875868.
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Swetha Parvatha Reddy Chandrasekhara
Department of Information Science and Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India.
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Swetha Parvatha Reddy Chandrasekhara, Srivinay, Sreevidya B S and Rudramurthy V C, “A Contemporary CNN based Classifier Approach for Intelligent Diagnostic Systems”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505013.
