Vaiyapuri Thavavel, Dutta Ashit Kumar, Punithavathi I S Hephzi, Duraipandy P, Alotaibi Saud S, Alsolai Hadeel, Mohamed Abdullah, Mahgoub Hany
Department of Computer Sciences, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia.
Healthcare (Basel). 2022 Apr 3;10(4):677. doi: 10.3390/healthcare10040677.
Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conclusion. Pancreatic cancer is a lethal type of cancer, and its prediction is ineffective with current techniques. Automated detection and classification of pancreatic tumors can be provided by the computer-aided diagnosis (CAD) model using radiological images such as computed tomography (CT) and magnetic resonance imaging (MRI). The recently developed machine learning (ML) and deep learning (DL) models can be utilized for the automated and timely detection of pancreatic cancer. In light of this, this article introduces an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images. The major intention of the IDLDMS-PTC technique is to examine the CT images for the existence of pancreatic tumors. The IDLDMS-PTC model derives an emperor penguin optimizer (EPO) with multilevel thresholding (EPO-MLT) technique for pancreatic tumor segmentation. Additionally, the MobileNet model is applied as a feature extractor with optimal auto encoder (AE) for pancreatic tumor classification. In order to optimally adjust the weight and bias values of the AE technique, the multileader optimization (MLO) technique is utilized. The design of the EPO algorithm for optimal threshold selection and the MLO algorithm for parameter tuning shows the novelty. A wide range of simulations was executed on benchmark datasets, and the outcomes reported the promising performance of the IDLDMS-PTC model on the existing methods.
决策医疗系统（DMS）是指医疗保健领域中决策技术的设计。它们涉及采用与某些过程相关的思想和决策的程序，如数据采集、处理、判断和结论。胰腺癌是一种致命的癌症类型，目前的技术对其预测效果不佳。计算机辅助诊断（CAD）模型可以利用计算机断层扫描（CT）和磁共振成像（MRI）等放射图像对胰腺肿瘤进行自动检测和分类。最近开发的机器学习（ML）和深度学习（DL）模型可用于胰腺癌的自动及时检测。鉴于此，本文介绍了一种使用CT图像的智能深度学习决策医疗系统用于胰腺肿瘤分类（IDLDMS-PTC）。IDLDMS-PTC技术的主要目的是检查CT图像中是否存在胰腺肿瘤。IDLDMS-PTC模型推导了一种用于胰腺肿瘤分割的带多级阈值处理的帝企鹅优化器（EPO-MLT）技术。此外，MobileNet模型被用作特征提取器，并结合最优自动编码器（AE）用于胰腺肿瘤分类。为了最优地调整AE技术的权重和偏差值，采用了多领导者优化（MLO）技术。用于最优阈值选择的EPO算法和用于参数调整的MLO算法的设计体现了其新颖性。在基准数据集上进行了广泛的仿真，结果表明IDLDMS-PTC模型在现有方法上具有良好的性能。
