An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique
The prostate cancer is a deadly form of cancer that assassinates a significant number of men due of its mediocre identification process. Images from people with cancer include important and intricate details that are difficult for conventional diagnostic methods to extract. This work establishes a...
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| Format: | Article |
| Language: | English |
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College of Education, Al-Iraqia University
2024-02-01
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| Series: | Iraqi Journal for Computer Science and Mathematics |
| Subjects: | |
| Online Access: | https://journal.esj.edu.iq/index.php/IJCM/article/view/1400 |
| _version_ | 1797294360968036352 |
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| author | Sathesh Abraham Leo E Nattar Kannan K |
| author_facet | Sathesh Abraham Leo E Nattar Kannan K |
| author_sort | Sathesh Abraham Leo E |
| collection | DOAJ |
| description |
The prostate cancer is a deadly form of cancer that assassinates a significant number of men due of its mediocre identification process. Images from people with cancer include important and intricate details that are difficult for conventional diagnostic methods to extract. This work establishes a novel Automated Prostate-cancer Prediction System (APPS) model for the goal of detecting and classifying prostate cancer utilizing MRI imaging sequences. The supplied medical image is normalized using a Coherence Diffusion Filtering (CDFilter) approach for improved quality and contrast. The appropriate properties are also extracted from the normalized image using the morphological and texture feature extraction approach, which helps to increase the classifier's accuracy. In order to train the classifier, the most important properties are also selected utilizing the cutting-edge Dragon Fly Optimized Feature Selection (DFO-FS) algorithm. Using this method greatly improves the classifier's overall disease diagnosis performance in less time and with faster processing. More specifically, the provided MRI input data are used to categorize the prostate cancer-affected and healthy tissues using the new Convoluted Gated Axial Attention Learning Model (ConGA2L) based on the selected features. This study compares and validates the performance of the APPS model by looking at several aspects using publicly available prostate cancer data.
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| first_indexed | 2024-03-07T21:30:10Z |
| format | Article |
| id | doaj.art-cb7d65fe2ab7490ba213a77f1ec74dcf |
| institution | Directory Open Access Journal |
| issn | 2958-0544 2788-7421 |
| language | English |
| last_indexed | 2024-03-07T21:30:10Z |
| publishDate | 2024-02-01 |
| publisher | College of Education, Al-Iraqia University |
| record_format | Article |
| series | Iraqi Journal for Computer Science and Mathematics |
| spelling | doaj.art-cb7d65fe2ab7490ba213a77f1ec74dcf2024-02-27T01:58:34ZengCollege of Education, Al-Iraqia UniversityIraqi Journal for Computer Science and Mathematics2958-05442788-74212024-02-015110.52866/ijcsm.2024.05.01.022An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging TechniqueSathesh Abraham Leo E0Nattar Kannan K1Research Scholar, Department of Computer Science and Engineering, Saveetha school of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai-602105, Tamil Nadu, India Professor, Department of Computer Science and Engineering, Saveetha school of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai-602105, Tamil Nadu, India. The prostate cancer is a deadly form of cancer that assassinates a significant number of men due of its mediocre identification process. Images from people with cancer include important and intricate details that are difficult for conventional diagnostic methods to extract. This work establishes a novel Automated Prostate-cancer Prediction System (APPS) model for the goal of detecting and classifying prostate cancer utilizing MRI imaging sequences. The supplied medical image is normalized using a Coherence Diffusion Filtering (CDFilter) approach for improved quality and contrast. The appropriate properties are also extracted from the normalized image using the morphological and texture feature extraction approach, which helps to increase the classifier's accuracy. In order to train the classifier, the most important properties are also selected utilizing the cutting-edge Dragon Fly Optimized Feature Selection (DFO-FS) algorithm. Using this method greatly improves the classifier's overall disease diagnosis performance in less time and with faster processing. More specifically, the provided MRI input data are used to categorize the prostate cancer-affected and healthy tissues using the new Convoluted Gated Axial Attention Learning Model (ConGA2L) based on the selected features. This study compares and validates the performance of the APPS model by looking at several aspects using publicly available prostate cancer data. https://journal.esj.edu.iq/index.php/IJCM/article/view/1400Prostate Cancer Detection, Artificial Intelligence (AI), Deep Learning, Magnetic Resonance Image (MRI), Automated Prostate-cancer Prediction System (APPS), Coherence Diffusion Filtering (CDFilter), Dragon Fly Optimized Feature Selection (DFO-FS), and Classification |
| spellingShingle | Sathesh Abraham Leo E Nattar Kannan K An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique Iraqi Journal for Computer Science and Mathematics Prostate Cancer Detection, Artificial Intelligence (AI), Deep Learning, Magnetic Resonance Image (MRI), Automated Prostate-cancer Prediction System (APPS), Coherence Diffusion Filtering (CDFilter), Dragon Fly Optimized Feature Selection (DFO-FS), and Classification |
| title | An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique |
| title_full | An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique |
| title_fullStr | An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique |
| title_full_unstemmed | An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique |
| title_short | An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique |
| title_sort | automated prostate cancer prediction system apps based on advanced dfo conga2l model using mri imaging technique |
| topic | Prostate Cancer Detection, Artificial Intelligence (AI), Deep Learning, Magnetic Resonance Image (MRI), Automated Prostate-cancer Prediction System (APPS), Coherence Diffusion Filtering (CDFilter), Dragon Fly Optimized Feature Selection (DFO-FS), and Classification |
| url | https://journal.esj.edu.iq/index.php/IJCM/article/view/1400 |
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