Department of Computer Science, College of Arts and Sciences at Tabarjal, Jouf University, Jouf, Saudi Arabia.
STIH, Sorbonne Universite, Paris, France.
J Healthc Eng. 2022 Mar 21;2022:8950243. doi: 10.1155/2022/8950243. eCollection 2022.
Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients and their families, researchers, and decision-makers. Thus, any innovations that improve the diagnostic process while maintaining quality and safety are crucial to the development of the healthcare field. Many diseases can be tentatively diagnosed during their initial stages. In this study, all developed techniques were applied to tuberculosis (TB). Thus, we propose an optimized machine learning-based model that extracts optimal texture features from TB-related images and selects the hyper-parameters of the classifiers. Increasing the accuracy rate and minimizing the number of characteristics extracted are our goals. In other words, this is a multitask optimization issue. A genetic algorithm (GA) is used to choose the best features, which are then fed into a support vector machine (SVM) classifier. Using the ImageCLEF 2020 data set, we conducted experiments using the proposed approach and achieved significantly higher accuracy and better outcomes in comparison with the state-of-the-art works. The obtained experimental results highlight the efficiency of modified SVM classifier compared with other standard ones.
计算机科学在现代动态健康系统中发挥着重要作用。鉴于诊断过程的协作性质，计算机技术为医疗保健专业人员和组织以及患者及其家属、研究人员和决策者提供了重要服务。因此，任何提高诊断过程质量和安全性的创新对于医疗保健领域的发展都是至关重要的。许多疾病在最初阶段就可以进行初步诊断。在这项研究中，所有开发的技术都应用于结核病（TB）。因此，我们提出了一种基于机器学习的优化模型，该模型从与结核病相关的图像中提取最佳纹理特征，并选择分类器的超参数。提高准确率和最小化提取特征的数量是我们的目标。换句话说，这是一个多任务优化问题。遗传算法（GA）用于选择最佳特征，然后将其输入支持向量机（SVM）分类器。使用 ImageCLEF 2020 数据集，我们使用提出的方法进行了实验，与最先进的工作相比，我们取得了显著更高的准确率和更好的结果。所获得的实验结果突出了改进后的 SVM 分类器与其他标准分类器相比的效率。
