Detection of coronavirus Disease (COVID-19) based on Deep Features

The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep learning based methodology is suggested for detection of coronavirus infected patient using X-ray images. The support vector machine classifies the corona affected X-ray images from others using the deep feature. The methodology is beneficial for the medical practitioner for diagnosis of coronavirus infected patient. The suggested classification model, i.e. resnet50 plus SVM achieved accuracy, FPR, F1 score, MCC and Kappa are 95.38%,95.52%, 91.41% and 90.76% respectively for detecting COVID-19 (ignoring SARS, MERS and ARDS). The classification model ResNet50 plus SVM is superior compared to other classification models. The result is based on the data available in the repository of GitHub, Kaggle and Open-i as per their validated X-ray images. Keywordcoronavirus, COVID-19, diagnosis, deep features, SVM.

Among the causing pathogens for respiratory diseases, CoV is become the dangerous one because of its serial interval (5 to 7.5) and reproductive rate (2 to 3) [2]. The CoV belongs to single-stranded RNA viruses (+ssRNA) family mostly observed in animals [3,4]. The analysis carried out till date, the viruses have no species barrier and can cause severe diseases like MERS and SARS. The coronavirus infection can provoke SARS that is severe enough to be called Acute respiratory distress syndrome (ARDS). In general, estimates suggest that 2% of the population are healthy carriers of a CoV and that these viruses are responsible for about 5% to 10% of acute respiratory infections [5]. COVID-19 spreads more easily than SARS and have symptoms like other coronaviruses. Figure 1 shows the distribution of COVID-19 cases worldwide, as of 17 March 2020 [6]. Figure 2 shows the distribution of COVID-19 cases by continent (except China), as of 17 March 2020 (according to the applied case definition and testing strategies in the affected countries) [6].  The test of COVID-19 is currently a difficult task because of unavailability of diagnosis system everywhere, which is causing panic. Because of the limited availability of COVID-19 testing kits, we need to rely on other diagnosis measures. Since COVID-19 attacks the epithelial cells that line our respiratory tract, we can use X-rays to analyse the health of a patient's lungs. The medical practitioner frequently uses X-ray images to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. And almost in all hospitals have X-ray imaging machines, it could be possible to use Xrays to test for COVID-19 without the dedicated test kits. Again, a drawback is that X-ray analysis requires a radiology expert and takes significant time, which is precious when people are sick around the world. Therefore, developing an automated analysis system is necessary to save medical professionals valuable time. The chest X-ray images of COVID-19 + are shown in Figure 1.
In this paper, a system based on deep CNNs is developed for the identification of COVID-19 as a classification task. In this study, we prepared two sets of datasets.  Table   2.

Methodology
Deep feature extraction is based on the extraction of features acquired from a pre-trained CNN [32].
The deep features are extracted from fully connected layer and feed to the classifier for training purpose.
The deep features obtained from each CNN networks are used by SVM classifier. After that, the classification is performed, and the performance of all classification models are measured. The rice leaf disease identification model based on deep features by SVM classifier is shown in Figure 2. The deep features of CNN models are extracted from a particular layer and feature vector is obtained.
The features are fed to the SVM classifier for rice disease identification purpose. The feature layer and feature vector are detailed in Table 3.

Results and Discussion
In this study, we examined the performance of classification models for identification COVID-19 + based on eleven number of CNN models. The experimental studies were implemented using the MATLAB 2019a deep learning toolbox. All applications were run on a laptop, i.e. Acer Predator Helios 300 Core i5 8th Gen -(8 GB/1 TB HDD/128 GB SSD/Windows 10 Home/4 GB Graphics) and equipped with NVIDIA GeForce GTX 1050Ti. The measurement of performance of each classifier is measured in terms of Accuracy, Sensitivity, Specificity, False positive rate (FPR), F1 Score, MCC and Kappa. In addition, this experimentation used One-Vs-all approach and linear SVM as the SVM classifier parameter. The results reported in Table 4 and Table 5 are based on the average value of 100 independent simulations. The training, validation and testing ration is 60:20:20 and adapted randomized selection for training, validation and testing in each execution. The results reported in Table 4 and   respectively.

Conclusion
The content of the manuscript about the coronavirus is based on the data available in WHO, European The proposed classification model for detection of COVID-19 is achieved 95.38% of accuracy.