Ragab Mahmoud, Kateb Faris, El-Sawy E K, Binyamin Sami Saeed, Al-Rabia Mohammed W, A Mansouri Rasha
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo 11884, Egypt.
Healthcare (Basel). 2023 Feb 16;11(4):590. doi: 10.3390/healthcare11040590.
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.
前列腺癌（PCa）正成为男性中最常见的癌症之一，且导致的死亡人数更多。由于肿瘤块的复杂性，放射科医生难以准确识别PCa。多年来，已经制定了几种PCa检测方法，但这些方法无法有效地识别癌症。人工智能（AI）既拥有模拟自然或生物现象的信息技术，又具备解决问题的人类智能。AI技术已在医疗保健领域广泛应用，包括3D打印、疾病诊断、健康监测、医院调度、临床决策支持、分类与预测以及医学数据分析。这些应用显著提高了医疗服务的成本效益和准确性。本文介绍了一种基于深度学习的前列腺癌分类阿基米德优化算法（AOADLB - P2C）模型，用于磁共振成像（MRI）图像。所提出的AOADLB - P2C模型检查MRI图像以识别PCa。为实现这一目标，AOADLB - P2C模型分两个阶段进行预处理：基于自适应中值滤波（AMF）的去噪和对比度增强。此外，所提出的AOADLB - P2C模型通过带有均方根传播（RMSProp）优化器的密集连接网络（DenseNet - 161）模型提取特征。最后，所提出的AOADLB - P2C模型使用带有最小二乘支持向量机（LS - SVM）方法的阿基米德优化算法（AOA）对PCa进行分类。所提出的AOADLB - P2C模型的模拟值使用基准MRI数据集进行测试。对比实验结果表明AOADLB - P2C模型相对于其他近期方法有所改进。
