Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks

Abstract

Since December 2019, the appearance of an outbreak of a novel coronavirus disease namely COVID-19 and which is previously known as 2019-nCoV. COVID-19 is a type of coronavirus that leads to the general destruction of respiratory systems and a severe respiratory symptom which are associated with highly Intensive Care Unit (ICU) admissions and death. Like any disease, the early diagnosis of coronavirus leads to limit its wide-spreading and increases the recovery rates of patients. The gold standard of COVID-19 detection is the real-time reverse transcription-polymerase chain reaction (RT-PCR) which has been used by the clinician to discover the presence or absence of this type of virus. The clinicians report that this technique has a low positive rate in the early stage of this disease. Based on this, the clinicians were forced to use another way to help in the early diagnosis of COVID-2019. So, the clinician's attention moved towards the medical imaging modalities especially the computed Tomography (CT) and X-ray chest images. Both modalities show that there is a change in the lungs in the case of COVID-19 that is different from any other type of pneumonic disease. Therefore, this research targeted toward employing different Artificial Intelligence (AI) techniques to propose a system for early detection of COVID-19 using chest X-ray images. These images are classified using different AI algorithms and a combination of them, then their performance was evaluated to recognize the best of them. These algorithms include a convolutional neural network (CNN), Softmax, support vector machine (SVM), Random Forest, and K nearest neighbor (KNN). Here CNN is into two scenarios, the first one to classify the X-ray images using a softmax classifier, and the second one to extract automated features from the images and pass these features to other classifiers (SVM, RFF, and KNN). According to the results, the performance of all classifiers is good and most of them record accuracy, sensitivity, specificity, and precision of more than 98%.

Deep learning based detection and analysis of COVID-19 on chest X-ray images

“…In [20], the author proposed a hybrid system based on artificial intelligence, which specially used machine learning and deep learning algorithms (i.e., Convolutional Neural Network (CNN) using softmax classifier). The proposed system is specially implemented for detecting Covid-19 cases using chest X-ray images.…”

Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey

“…5 illustrates the results of the studies that presented the most common metrics, i.e., accuracy, specificity, and sensitivity. As evident, Alqudah et al [56] achieved the highest performance with respect to all measures. The methods proposed by Razzak et al [34] and Kumar et al [57] also achieved comparable results for all metrics; as demonstrated in Table V, these two studies dealt with class imbalance by including an equal number of samples for each class and employing the synthetic minority oversampling (SMOTE) technique, respectively.…”

“…From the reviewed studies, Alqudah et al [51] used ShuffleNet for the automatic extraction of features, which were then fed to four different classifiers: Random Forest, Softmax, SVM, and KNN. The accuracies achieved by these classifiers with the ShuffleNet features were 80%, 99.35%, 95.81, and 99.35%, respectively.…”

“…Thus, several studies considered the class imbalance problem by employing different techniques. Alqudah et al [56] handled this problem by keeping the number of samples in each class equal to 310. Other studies such as [59] and [61] utilized data augmentation.…”

“…CAM is a visualization technique that replaces the fully connected layers with the global average pooling and convolutional layers to create class-specific feature maps because the global average pooling layer enables localization in CNNs [70]. CAM was employed in three studies- [73], [51], and [79]-as shown in Fig. 6.…”