Ramanujan Computing Centre, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India.
Alumna, Department of Information Science and Technology, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India.
J Xray Sci Technol. 2024;32(2):253-269. doi: 10.3233/XST-230196.
The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented.
The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features.
Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered.
The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%.
新型冠状病毒肺炎是一种由新型冠状病毒（严重急性呼吸综合征冠状病毒 2，SARS-CoV-2）感染引起的严重且高度传染性疾病。
建立一种计算机辅助诊断（CAD）系统，以协助医生从胸部计算机断层扫描（CT）切片中诊断新冠病毒肺炎。
采用 Otsu 阈值法对肺组织进行分割。将新冠病毒病变区域标注为感兴趣区域（ROI），然后进行纹理和形状提取。得到的特征被存储为特征向量，并分为 80:20 的训练集和测试集。为了选择最佳特征，采用鲸鱼优化算法（WOA）和支持向量机（SVM）分类器的准确率来选择最优特征。采用多层感知机（MLP）分类器对选择的特征进行分类。
使用实时数据集对所提出的系统与现有的八个基准机器学习分类器进行了比较实验，结果表明，所提出的系统的准确率为 88.94%，优于基准分类器的结果。进行了统计分析，即 Friedman 检验、Mann-Whitney U 检验和 Kendall 秩相关系数检验，表明该方法对所考虑的新型数据集有显著影响。
未进行特征选择的 MLP 分类器的准确率为 80.40%，而使用 WOA 进行特征选择的准确率为 88.94%。
