Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

Abstract

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.

Multi-Channel Based Image Processing Scheme for Pneumonia Identification

“…More importantly, as explained above, the stated result in terms of Receiver Operating Characteristic (ROC) and precision-recall can aid expert radiologist in striking a balance between accuracy and precision. -90.9 100.0 Apostolopoulos et al [27] 98.0 92.9 98.8 Xu et al [28] 86.7 86.9 -Habib et al [29] 98.93 --Chouchan et al [30] 96.4 99.6 -Yamaç et al [35] 86.5 79.2 90.7 Wang et al [36] 93.3 90.7 95.5 Li et al [37] 96.9 97.8 94.9 J.K. K. Singh and A. Singh [38] 95.8 96.1 95.7 Yang et al [39] 88.4 64.7 92.9 Wang et al [40] 94.5 94.7 97.3 Alsharif et al [41] 99.7 99.7 99.8 Alqudah et al [42] 93.9 93.2 96.6 Alquran et al [43] 93.1 92.9 96.4 Masad et al [44] 98.9 Nevertheless, other methods have shown better performance for example, Pneumoni-aNet model [41]. PneumoniaNet model proposed by Alsharif et al [41] uses CXR images to distinguish normal radiographic images from those with features consistent with viral or bacterial pneumonia in the pediatric group aged one-five years with a 99.72% accuracy, 99.74% sensitivity, 99.85% specificity, 99.7% precision, and 98.12% AUC.…”

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

“…Chest radiographs are used to diagnose patients with Covid-19 infections, as the virus primarily affects the lungs. As a result, there are several deep learning methods that have investigated the detection of covid 19 infection using publicly available chest radiographs datasets [7].…”

PneumoniaNet: Automated Detection and Classification of Pediatric Pneumonia Using Chest X-ray Images and CNN Approach

“…For example, Harsh Agrawal used pre-processing techniques as an initial step before using the ResNet50 v2 deep learning structure, which lead to improving the detection accuracy of pneumonia in CXR images to 96% [18]. Alquran et al exploited texture features and traditional machine learning algorithms to classify Chest-X-rays (CXR) into three classes, Pneumonia regardless of its source including viral or bacterial, COVID 19, and normal chest images; they obtained a 93.1% accuracy among all classes [19]. Rajasenbagam et al also utilized deep learning to detect pneumonia infection using Chest-X-ray (CXR) images.…”