Department of Computer Science, University of the Punjab, Lahore, Pakistan.
School of Computer Science and Technology, University of Science and Technology of China, China.
Curr Med Imaging. 2024;20:e15734056305021. doi: 10.2174/0115734056305021240603114137.
The second highest cause of death among males is Prostate Cancer (PCa) in America. Over the globe, it's the usual case in men, and the annual PCa ratio is very surprising. Identical to other prognosis and diagnostic medical systems, deep learning-based automated recognition and detection systems (i.e., Computer Aided Detection (CAD) systems) have gained enormous attention in PCA.
These paradigms have attained promising results with a high segmentation, detection, and classification accuracy ratio. Numerous researchers claimed efficient results from deep learning-based approaches compared to other ordinary systems that utilized pathological samples.
This research is intended to perform prostate segmentation using transfer learning-based Mask R-CNN, which is consequently helpful in prostate cancer detection.
Lastly, limitations in current work, research findings, and prospects have been discussed.
在美国，男性的第二大死因是前列腺癌（PCa）。在全球范围内，前列腺癌在男性中很常见，每年的前列腺癌发病率都令人惊讶。与其他预后和诊断医学系统一样，基于深度学习的自动识别和检测系统（即计算机辅助检测（CAD）系统）在 PCA 中受到了极大的关注。
这些范例在分割、检测和分类精度方面取得了很有前景的结果。许多研究人员声称，与其他利用病理样本的普通系统相比，基于深度学习的方法能够得到更有效的结果。
本研究旨在使用基于迁移学习的 Mask R-CNN 进行前列腺分割，这对前列腺癌的检测很有帮助。
最后，讨论了当前工作、研究结果和前景中的局限性。
