Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions

Prostate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in...

Full description

Bibliographic Details
Main Authors: Rachid Sammouda, Abdu Gumaei, Ali El-Zaart
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2021/9955174
id doaj-d3e0f8c4c7a448ac9eab1754cf8be383
record_format Article
spelling doaj-d3e0f8c4c7a448ac9eab1754cf8be3832021-07-19T01:04:52ZengHindawi LimitedMathematical Problems in Engineering1563-51472021-01-01202110.1155/2021/9955174Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future DirectionsRachid Sammouda0Abdu Gumaei1Ali El-Zaart2Department of Computer ScienceDepartment of Computer ScienceDepartment of Mathematics and Computer ScienceProstate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in the estimation of PCa volume in the gland, we provide a more detailed review of methodologies that use specific parameters for prostate tissue representation. After collecting over 200 researches on image-based systems for diagnosing prostate cancer, in this paper, we provide a detailed review of existing computer-aided diagnosis (CAD) methods and approaches to identify prostate cancer from images generated using Near-Infrared (NIR), Mid-Infrared (MIR), and Magnetic Resonance Imaging (MRI) techniques. Furthermore, we introduce two research methodologies to build intelligent CAD systems. The first methodology applies a fuzzy integral method to maintain the diversity and capacity of different classifiers aggregation to detect PCa tumor from NIR and MIR images. The second methodology investigates a typical workflow for developing an automated prostate cancer diagnosis using MRI images. Essentially, CAD development remains a helpful tool of radiology for diagnosing prostate cancer disease. Nonetheless, a complete implementation of effective and intelligent methods is still required for the PCa-diagnostic system. While some CAD applications work well, some limitations need to be solved for automated clinical PCa diagnostic. It is anticipated that more advances should be made in computational image analysis and computer-assisted approaches to satisfy clinical needs shortly in the coming years.http://dx.doi.org/10.1155/2021/9955174
collection DOAJ
language English
format Article
sources DOAJ
author Rachid Sammouda
Abdu Gumaei
Ali El-Zaart
spellingShingle Rachid Sammouda
Abdu Gumaei
Ali El-Zaart
Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions
Mathematical Problems in Engineering
author_facet Rachid Sammouda
Abdu Gumaei
Ali El-Zaart
author_sort Rachid Sammouda
title Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions
title_short Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions
title_full Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions
title_fullStr Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions
title_full_unstemmed Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions
title_sort intelligent computer-aided prostate cancer diagnosis systems: state-of-the-art and future directions
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1563-5147
publishDate 2021-01-01
description Prostate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in the estimation of PCa volume in the gland, we provide a more detailed review of methodologies that use specific parameters for prostate tissue representation. After collecting over 200 researches on image-based systems for diagnosing prostate cancer, in this paper, we provide a detailed review of existing computer-aided diagnosis (CAD) methods and approaches to identify prostate cancer from images generated using Near-Infrared (NIR), Mid-Infrared (MIR), and Magnetic Resonance Imaging (MRI) techniques. Furthermore, we introduce two research methodologies to build intelligent CAD systems. The first methodology applies a fuzzy integral method to maintain the diversity and capacity of different classifiers aggregation to detect PCa tumor from NIR and MIR images. The second methodology investigates a typical workflow for developing an automated prostate cancer diagnosis using MRI images. Essentially, CAD development remains a helpful tool of radiology for diagnosing prostate cancer disease. Nonetheless, a complete implementation of effective and intelligent methods is still required for the PCa-diagnostic system. While some CAD applications work well, some limitations need to be solved for automated clinical PCa diagnostic. It is anticipated that more advances should be made in computational image analysis and computer-assisted approaches to satisfy clinical needs shortly in the coming years.
url http://dx.doi.org/10.1155/2021/9955174
work_keys_str_mv AT rachidsammouda intelligentcomputeraidedprostatecancerdiagnosissystemsstateoftheartandfuturedirections
AT abdugumaei intelligentcomputeraidedprostatecancerdiagnosissystemsstateoftheartandfuturedirections
AT alielzaart intelligentcomputeraidedprostatecancerdiagnosissystemsstateoftheartandfuturedirections
_version_ 1721295486275551232