Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging
Prostate cancer (PCa) is becoming one of the most frequently occurring cancers among men and causes an even greater number of deaths. Due to the complexity of tumor masses, radiologists find it difficult to identify PCa accurately. Over the years, several PCa-detecting methods have been formulated,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/11/4/590 |
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| author | Mahmoud Ragab Faris Kateb E. K. El-Sawy Sami Saeed Binyamin Mohammed W. Al-Rabia Rasha A. Mansouri |
| author_facet | Mahmoud Ragab Faris Kateb E. K. El-Sawy Sami Saeed Binyamin Mohammed W. Al-Rabia Rasha A. Mansouri |
| author_sort | Mahmoud Ragab |
| collection | DOAJ |
| description | Prostate cancer (PCa) is becoming one of the most frequently occurring cancers among men and causes an even greater number of deaths. Due to the complexity of tumor masses, radiologists find it difficult to identify PCa accurately. Over the years, several PCa-detecting methods have been formulated, but these methods cannot identify cancer efficiently. Artificial Intelligence (AI) has both information technologies that simulate natural or biological phenomena and human intelligence in addressing issues. AI technologies have been broadly implemented in the healthcare domain, including 3D printing, disease diagnosis, health monitoring, hospital scheduling, clinical decision support, classification and prediction, and medical data analysis. These applications significantly boost the cost-effectiveness and accuracy of healthcare services. This article introduces an Archimedes Optimization Algorithm with Deep Learning-based Prostate Cancer Classification (AOADLB-P2C) model on MRI images. The presented AOADLB-P2C model examines MRI images for the identification of PCa. To accomplish this, the AOADLB-P2C model performs pre-processing in two stages: adaptive median filtering (AMF)-based noise removal and contrast enhancement. Additionally, the presented AOADLB-P2C model extracts features via a densely connected network (DenseNet-161) model with a root-mean-square propagation (RMSProp) optimizer. Finally, the presented AOADLB-P2C model classifies PCa using the AOA with a least-squares support vector machine (LS-SVM) method. The simulation values of the presented AOADLB-P2C model are tested using a benchmark MRI dataset. The comparative experimental results demonstrate the improvements of the AOADLB-P2C model over other recent approaches. |
| first_indexed | 2024-03-11T08:46:10Z |
| format | Article |
| id | doaj.art-b8c812c4775a4b519040809beac91955 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-11T08:46:10Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-b8c812c4775a4b519040809beac919552023-11-16T20:47:21ZengMDPI AGHealthcare2227-90322023-02-0111459010.3390/healthcare11040590Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance ImagingMahmoud Ragab0Faris Kateb1E. K. El-Sawy2Sami Saeed Binyamin3Mohammed W. Al-Rabia4Rasha A. Mansouri5Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaInformation Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaFaculty of Earth Sciences, King Abdulaziz University, Jeddah 21589, Saudi ArabiaComputer and Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Medical Microbiology and Parasitolog, Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi ArabiaPrince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaProstate cancer (PCa) is becoming one of the most frequently occurring cancers among men and causes an even greater number of deaths. Due to the complexity of tumor masses, radiologists find it difficult to identify PCa accurately. Over the years, several PCa-detecting methods have been formulated, but these methods cannot identify cancer efficiently. Artificial Intelligence (AI) has both information technologies that simulate natural or biological phenomena and human intelligence in addressing issues. AI technologies have been broadly implemented in the healthcare domain, including 3D printing, disease diagnosis, health monitoring, hospital scheduling, clinical decision support, classification and prediction, and medical data analysis. These applications significantly boost the cost-effectiveness and accuracy of healthcare services. This article introduces an Archimedes Optimization Algorithm with Deep Learning-based Prostate Cancer Classification (AOADLB-P2C) model on MRI images. The presented AOADLB-P2C model examines MRI images for the identification of PCa. To accomplish this, the AOADLB-P2C model performs pre-processing in two stages: adaptive median filtering (AMF)-based noise removal and contrast enhancement. Additionally, the presented AOADLB-P2C model extracts features via a densely connected network (DenseNet-161) model with a root-mean-square propagation (RMSProp) optimizer. Finally, the presented AOADLB-P2C model classifies PCa using the AOA with a least-squares support vector machine (LS-SVM) method. The simulation values of the presented AOADLB-P2C model are tested using a benchmark MRI dataset. The comparative experimental results demonstrate the improvements of the AOADLB-P2C model over other recent approaches.https://www.mdpi.com/2227-9032/11/4/590artificial intelligencehealthcareprostate cancermedical imagingdeep learning |
| spellingShingle | Mahmoud Ragab Faris Kateb E. K. El-Sawy Sami Saeed Binyamin Mohammed W. Al-Rabia Rasha A. Mansouri Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging Healthcare artificial intelligence healthcare prostate cancer medical imaging deep learning |
| title | Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging |
| title_full | Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging |
| title_fullStr | Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging |
| title_full_unstemmed | Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging |
| title_short | Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging |
| title_sort | archimedes optimization algorithm with deep learning based prostate cancer classification on magnetic resonance imaging |
| topic | artificial intelligence healthcare prostate cancer medical imaging deep learning |
| url | https://www.mdpi.com/2227-9032/11/4/590 |
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