Azad Abul Kalam, Ahmed Imtiaz, Ahmed Mosabber Uddin
Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh.
Diagnostics (Basel). 2023 Feb 3;13(3):574. doi: 10.3390/diagnostics13030574.
The virus responsible for COVID-19 is mutating day by day with more infectious characteristics. With the limited healthcare resources and overburdened medical practitioners, it is almost impossible to contain this virus. The automatic identification of this viral infection from chest X-ray (CXR) images is now more demanding as it is a cheaper and less time-consuming diagnosis option. To that cause, we have applied deep learning (DL) approaches for four-class classification of CXR images comprising COVID-19, normal, lung opacity, and viral pneumonia. At first, we extracted features of CXR images by applying a local binary pattern (LBP) and pre-trained convolutional neural network (CNN). Afterwards, we utilized a pattern recognition network (PRN), support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (KNN) classifiers on the extracted features to classify aforementioned four-class CXR images. The performances of the proposed methods have been analyzed rigorously in terms of classification performance and classification speed. Among different methods applied to the four-class test images, the best method achieved classification performances with 97.41% accuracy, 94.94% precision, 94.81% recall, 98.27% specificity, and 94.86% F1 score. The results indicate that the proposed method can offer an efficient and reliable framework for COVID-19 detection from CXR images, which could be immensely conducive to the effective diagnosis of COVID-19-infected patients.
导致新冠肺炎的病毒正日复一日地发生变异，传染性越来越强。在医疗资源有限且医护人员负担过重的情况下，几乎不可能遏制这种病毒。从胸部X光（CXR）图像中自动识别这种病毒感染的需求现在变得更加迫切，因为这是一种成本更低且耗时更少的诊断选择。为此，我们应用深度学习（DL）方法对包含新冠肺炎、正常、肺部 opacity（此处可能是笔误，应为opacity，意为不透明、浑浊等，这里结合语境可能是指肺部有病变影像表现）和病毒性肺炎的CXR图像进行四类分类。首先，我们通过应用局部二值模式（LBP）和预训练卷积神经网络（CNN）提取CXR图像的特征。之后，我们在提取的特征上使用模式识别网络（PRN）、支持向量机（SVM）、决策树（DT）、随机森林（RF）和k近邻（KNN）分类器对上述四类CXR图像进行分类。从分类性能和分类速度方面对所提出方法的性能进行了严格分析。在应用于四类测试图像的不同方法中，最佳方法实现了分类性能，准确率为97.41%，精确率为94.94%，召回率为94.81%，特异性为98.27%，F1分数为94.86%。结果表明，所提出的方法可以为从CXR图像中检测新冠肺炎提供一个高效且可靠的框架，这对新冠肺炎感染患者的有效诊断可能非常有帮助。
