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

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

Mahmoud Ragab  1   2 , Faris Kateb  1 , E K El-Sawy  3   4 , Sami Saeed Binyamin  5 , Mohammed W Al-Rabia  6   7 , Rasha A Mansouri  8   9
Affiliations

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

Mahmoud Ragab et al. Healthcare (Basel). .

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.

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Conflict of interest statement

The authors declare that they have no conflict of interest. The manuscript was written with contributions from all authors. All authors have given approval to the final version of the manuscript.

Figures

Figure 1
Figure 1
Overall working process of the AOADLB-P2C system.
Figure 2
Figure 2
SVM hyperplane.
Figure 3
Figure 3
Sample images.
Figure 4
Figure 4
Confusion matrices of the proposed AOADLB-P2C system: (a) Run-1, (b) Run-2, (c) Run-3, (d) Run-4, and (e) Run-5.
Figure 5
Figure 5
Average analysis of the AOADLB-P2C system: (a) Run-1, (b) Run-2, (c) Run-3, (d) Run-4, and (e) Run-5.
Figure 6
Figure 6
TACC and VACC analytical results of the AOADLB-P2C system.
Figure 7
Figure 7
TLS and VLS analytical results of the proposed AOADLB-P2C system.
Figure 8
Figure 8
Accuy and Fscore analytical results of the AOADLB-P2C system and other recent approaches.
Figure 9
Figure 9
Sensy and Specy analytical results of the proposed AOADLB-P2C system and other recent approaches.
See this image and copyright information in PMC

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