Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging

Mahmoud Ragab; Faris Kateb; E K El-Sawy; Sami Saeed Binyamin; Mohammed W Al-Rabia; Rasha A Mansouri

doi:10.3390/healthcare11040590

Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging

Healthcare (Basel). 2023 Feb 16;11(4):590. doi: 10.3390/healthcare11040590.

Authors

Mahmoud Ragab^{1

2}, Faris Kateb¹, E K El-Sawy^{3

4}, Sami Saeed Binyamin⁵, Mohammed W Al-Rabia^{6

7}, Rasha A Mansouri^{8

9}

Affiliations

¹ Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
² Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo 11884, Egypt.
³ Faculty of Earth Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁴ Geology Department, Faculty of Science, Al-Azhar University (Assiut branch), Assiut 71524, Egypt.
⁵ Computer and Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁶ Department of Medical Microbiology and Parasitolog, Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁷ Health Promotion Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁸ Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
⁹ Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Abstract

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.

Keywords: artificial intelligence; deep learning; healthcare; medical imaging; prostate cancer.

Grants and funding

The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia, funded this project under grant no. (PH: 030-145-1443).