Sachin Paithane
Sanika Padwalkar
Vivek Nikam
Prashant Pawar
Priti Warungse
Abstract
Prostate Cancer is a cancer occurs in prostate gland which is located in male reproductive system. According to the WHO (World Health Organization) the estimated cancer cases in the year 2020 is about 1.4 Million. Prostate cancer is one of the reason for death in men. In this paper we have presented Secondary verification tool for doctors or for normal users to check patient have a cancer or not. And also other things are disused in this paper like implementation of the Deep learning CNN (Convolutional Neural Network) for detection of prostate cancer using input as ultrasound image [1]. We have discussed why we have selected Deep learning CNN algorithm for detecting prostate cancer in our previous paper which is also in titled as prostate cancer detection using deep learning. We are also discussing which libraries are required for implementing this project and other tools and hardware requirement for this project.
Keywords: Prostate Cancer, Prostate Cancer Detection, Deep Learning, CNN, Deep Learning Model, Medical use.
[This article belongs to International Journal of Biomedical Innovations and Engineering(ijbie)]
References
- Artan and I. S. Yetik, 2012, “Prostate cancer localization using multiparametric MRI based on semisupervised techniques with automated seed initialization,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 6, pp. 1313–1323,doi: 10.1109/TITB.2012.2201731.
- Azizi et al., 2018, “Deep recurrent neural networks for prostate cancer detection: Analysis of temporal enhanced ultrasound,” IEEE Trans. Med. Imaging, vol. 37, no. 12, pp. 2695–2703, doi: 10.1109/TMI.2018.2849959.
- Brunese, F. Mercaldo, A. Reginelli, and A. Santone, 2019, “Prostate gleason score detection and cancer treatment through real-time formal verification,” IEEE Access, vol. 7, pp. 186236–186246, doi: 10.1109/ACCESS.2019.2961754.
- Feng et al., 2019, “A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 16, no. 6, pp. 1794–1801, doi: 10.1109/TCBB.2018.2835444.
- T. Kwak and S. M. Hewitt, 2017, “Lumen-based detection of prostate cancer via convolutional neural networks,” Med. Imaging 2017 Digit. Pathol., vol. 10140, p. 1014008, doi: 10.1117/12.2253513.
- Li et al., 2019, “Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images,” IEEE Trans. Med. Imaging, vol. 38, no. 4, pp. 945–954, doi: 10.1109/TMI.2018.2875868.
- Litjens, O. Debats, J. Barentsz, N. Karssemeijer, and H. Huisman, 2014, “Computer-aided detection of prostate cancer in MRI,” IEEE Trans. Med. Imaging, vol. 33, no. 5, pp. 1083–1092, doi: 10.1109/TMI.2014.2303821.
- Moradi, P. Abolmaesumi, D. R. Siemens, E. E. Sauerbrei, A. H. Boag, and P. Mousavi, 2009, “Augmenting detection of prostate cancer in transrectal ultrasound images using SVM and RF time series,” IEEE Trans. Biomed. Eng., vol. 56, no. 9, pp. 2214–2224, doi: 10.1109/TBME.2008.2009766.ICCRDA 2020 IOP Conf. Series: Materials Science and Engineering 1022 (2021) 012073 IOP Publishing doi:10.1088/1757-899X/1022/1/012073 10
- G. Schalk et al., 2017, “Contrast-Enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for Prostate Cancer Localization,” IEEE Trans. Biomed. Eng., vol. 64, no. 3, pp. 661–670, doi: 10.1109/TBME.2016.2571624rial Network for Prostate Cancer Segmentation,” IEEE Access, vol. 7, pp. 131448–131458, doi: 10.1109/ACCESS.2019.2939389
| Volume | 01 |
| Issue | 02 |
| Received | December 12, 2023 |
| Accepted | December 15, 2023 |
| Published | December 25, 2023 |