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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Bibliographic Details
Published in:Healthcare (Basel) 2023-02, Vol.11 (4), p.590
Main Authors: Ragab, Mahmoud, Kateb, Faris, El-Sawy, E K, Binyamin, Sami Saeed, Al-Rabia, Mohammed W, A Mansouri, Rasha
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biomarkers
Cancer therapies
Classification
Deep learning
Diabetic retinopathy
Diagnosis
Genomics
Health aspects
healthcare
Identification and classification
Machine learning
Magnetic resonance imaging
medical imaging
Neural networks
Optimization algorithms
Prostate cancer
Research methodology
Support vector machines
Ultrasonic imaging
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Summary: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.
ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare11040590