| Autor: |
Ayyad SM; Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt., Shehata M; BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA., Shalaby A; BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA., Abou El-Ghar M; Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt., Ghazal M; Department of Electrical and Computer Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates., El-Melegy M; Department of Electrical Engineering, Assiut University, Assiut 71511, Egypt., Abdel-Hamid NB; Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt., Labib LM; Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt., Ali HA; Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt., El-Baz A; BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA. |
| Abstrakt: |
Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images. |
