key: cord-0778677-hk6evzpl authors: Elkorany, Ahmed S.; Elsharkawy, Zeinab F. title: COVIDetection-Net: A Tailored COVID-19 Detection from Chest Radiography Images Using Deep Learning date: 2021-02-01 journal: Optik (Stuttg) DOI: 10.1016/j.ijleo.2021.166405 sha: d2283cb7a37190a2c14f2f4e1ba724e64df1c77b doc_id: 778677 cord_uid: hk6evzpl In this study, a medical system based on Deep Learning (DL) which we called “COVIDetection-Net” is proposed for automatic detection of new corona virus disease 2019 (COVID-19) infection from chest radiography images (CRIs). The proposed system is based on ShuffleNet and SqueezeNet architecture to extract deep learned features and Multiclass Support Vector Machines (MSVM) for detection and classification. Our dataset contains 1200 CRIs that collected from two different publicly available databases. Extensive experiments were carried out using the proposed model. The highest detection accuracy of 100% for COVID/NonCOVID, 99.72% for COVID/Normal/pneumonia and 94.44% for COVID/Normal/Bacterial pneumonia/Viral pneumonia have been obtained. The proposed system superior all published methods in recall, specificity, precision, F1-Score and accuracy. Confusion Matrix (CM) and Receiver Operation Characteristics (ROC) analysis are also used to depict the performance of the proposed model. Hence the proposed COVIDetection-Net can serve as an efficient system in the current state of COVID-19 pandemic and can be used in everywhere that are facing shortage of test kits. The COVID-19 that originated in Wuhan, China in December 2019 spread out across the world. It infected until now around 27.76 million people and around 902315 deaths all over the world J o u r n a l P r e -p r o o f [1] . As we all see the number of cases increasing rapidly, all countries are facing the shortage of resources to detect COVID-19. Here comes the need to available, cheap, automatic COVID-19 detection method. Applying deep Learning models on Chest radiography images (e.g., X-ray or computed tomography (CT) imaging) can help in detecting COVID-19. Currently, real-time reverse transcription poly-merase chain reaction (rRT-PCR) is the main screening method used for COVID-19 detection [2] . The radiography imaging sensitivity outperforms the usual PCR technique [3] [4] [5] [6] [7] . Also the X-ray systems are cheaper and more available in hospitals than CT scan systems [8, 9] . The CRIs based detection system have many advantages over usual blood [10] and PCR [2] exams. These techniques are fast and cheap compared to other techniques. Also, many cases can be tested in the same time. Moreover, the availability of radiology imaging systems in hospitals, makes radiography-based detection systems a suitable variant for COVID-19 testing kit shortage. In August 2020, large number of X-rays chest images for healthy and patients suffering from Covid-19 are publicly available, which enable us to examine and identify the possible patterns that may produce automatic diagnosis of COVID-19. Deep Learning (DL), which is a part of machine learning systems, is focused mainly on images' features extraction and classification on automated manner [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] . In recent years, machine and deep learning have become basic disciplines in artificial intelligence. Hence, DL has become a basic part of automated clinical decision making. In the paper, a new COVIDetection-Net system is proposed which is an automatic detection of COVID-19 infection from CRIs. The system depends on ShuffleNet and SqueezeNet architectures for feature extraction and MSVM for disease detection and classification. The utilized dataset contains 1200 CRIs. It contains chest images for healthy (i.e., Normal), and nonhealthy (patients infected with COVID, Bacterial pneumonia, or Viral pneumonia). Taking into account the experimental results the proposed COVIDetection-Net can serve as an efficient system in COVID-19 detection. The rest of the paper is organized as follows. Section 2 outlines the related work. Section 3 discusses the methods used in the proposed system. Section 4 shows the COVIDetection-Net experimental setup. Sections 5 and 6 present and discuss the COVIDetection-Net results. Finally section 7 outlines the conclusion J o u r n a l P r e -p r o o f Recently, many researchers proposed different techniques for COVID-19 detection from CRIs. Most researches focused on DL techniques to detect the COVID19 from CRI of patients. Some of them interested in COVID detection from non-COVID cases (binary classification) [8, 9, 11, [22] [23] [24] [25] [26] . Others concerned with thee-class classification (COVID vs Normal vs pneumonia) [11] , [12] [13] [14] [15] [16] [17] [18] [19] . But in fact, fewer focused on COVID19 detection using four-class classification (COVID vs Normal vs Bacterial pneumonia vs Viral pneumonia) [20, 21] . Brunese et al [8] introduced a COVID detection approach based on transfer learning of VGG-16 model. This model used to differentiate between healthy and disease X-ray images with accuracy of 96%. Then, it used for COVID detection from disease X-ray chest dataset with accuracy of 98%. Dipayan Das et. al [9] presented Truncated inception net to detect COVID-19 positive from non-COVID cases using CRIs. Detection accuracy of 99.9% is obtained. Hemdan et al [22] proposed a COVIDX-Net, DL framework, for automatic diagnosing COVID19 in CRIs. Seven-different convolutional neural networks (CNN) model are used in COVIDX-Net. The used DenseNet and VGG-19 provided the same accuracy of 90% for binary classification. Narin et al [23] used 5fold cross validation pre-trained models for coronavirus detection from CRIs. The highest accuracy of 98% is obtained using ResNet50 model. Panwar et al [24] presented DL based method nCOVnet for fast COVID19 detection from x-ray chest images. This method used the VGG net and transfer learning using five-different layers that achieved 97.2% detection accuracy. Sethy et al [11] extracted the deep features from the CRIs using the fully connected layer of the pre-trained models. Then, features matching has been done using SVM classifier. The authors utilized 13 different pre-trained net and the highest classification accuracy of 98.66% was obtained using ResNet50. Singh et. al [25] proposed a (CNN) to classify the chest CT patient's images to COVID-infected or not using multi objective differential evolution (MODE). This approach provided 93.5% accuracy. Tuncer et al [26] presented Residual Exemplar Local Binary Pattern (ResExLBP) features extraction method with iterative ReliefF (IRF) features selection for COVID19 detection from CRIs. An accuracy of 99.69% is achieved using SVM classifier. Apostolopoulos et. al [12] used transfer learning technique for binary and 3-class classification of COVID, normal and pneumonia CRIs. The produced classification accuracies were 97.4% and 92.85% for binary and 3-class classification, respectively when MobileNet v2 is accuracy for XGB and 97.3 for RF classifiers. Tree-class COVID classification method of CRIs was proposed in [14] . The method based on DL and 9-different CNN architectures. The best accuracy was 95% for two models. Ozturk et al [15] presented 5-cross validation DarkCovidNet for binary and 3-class classification of CRIs. The presented method provided accuracy of 98.08% and 87.02% for binary and 3-class cases, respectively. The concatenation network of Xception and ResNet50V2 is presented in [16] for detection of pneumonia and COVID19 from CRIs. The overall accuracy of 91.4% is obtained for the three classes (normal, COVID and pneumonia). Ucar et al [17] proposed COVIDiagnosis-Net for COVID19 diagnosis from CRIs. This approach based on deep Bayes-SqueezeNet that produced overall accuracy of 98.26%. Wang et al [18] introduces DCNN model called COVID-Net to detect COVID cases from CRIs. This model was designed for (normal vs pneumonia vs COVID) classification with overall accuracy of 92.4%. Li et. al [19] demonstrated COVID-Xpert based population screening to detect COVID19 cases from X-ray CRIs. The demonstrated method provided 88.9% classification accuracy. COVID19 detection using 4-class cases from CRIs is presented by Khan et al [20] . The authors presented DCNN model named, CoroNet, based on Xception architecture. The classification accuracy of 89.6% is produced for 4-class cases. Mahmud et al [21] designed CovXNet, CNN based architecture, for COVID19 detection and classification. A stacking algorithm is used for optimization of CovXNet prediction and 90.2% accuracy is obtained for 4class classification. Accordingly, it can be concluded that, many researchers studied the COVID detection and introduced different techniques for this problem. However, the most of them did not provide the This work focused in developing a detection model named COVIDetection-Net based on DL techniques for COVID 19 detection from CRIs using 4-class cases. The prepared dataset and the proposed COVIDetection-Net are explained in details as follow. To evaluate the proposed COVIDetection-Net, our dataset is created using two different publically CRIs databases. Firstly, a collection of CRIs from the Github repository was selected [27] . Then, Kaggle repository of normal and pneumonia CRIs was considered [28] . The two repository contains an open database of CRIs (chest X-ray or CT images) and is being updated regularly. A total of 1200 CRIs are selected from the Github and Kaggle repositories, in which 300 images are COVID19, 300 images are bacterial pneumonia, 300 images viral pneumonia and 300 images are normal cases. Table 1 summarized the prepared dataset. The samples of the prepared dataset are shown in Fig.1 . Deep networks (CNNs or DL) are useful in machine vision tasks. This made developments in many fields as industry [29] , Agriculture and medical disease diagnosis [8, 9, 11, [22] [23] [24] [25] [26] . The notability of these CNN networks comes from the useful and robust features that extracted from input images. Hence, the deep networks focus on infection detecting in CRIs and classifying these images into COVID-19, normal, bacterial pneumonia or viral pneumonia. Some of the most usable CNN are ResNet [11, 13, 16, 23] , VGG [8, 11, 12, 14] , Xception [16, 20] ,Inception [9, 23] , DesNet [11, 22] , ShuffleNet [29, 30] , and SqueezeNet [17, 31, 32] . These networks pretrained on ImageNet dataset. Shufflenet is a CNN also that outperform many networks in speed and accuracy metrics at the same computation condition [30] . In total, it composed of 172 layers including convolution layer, max pooling layer, three stages each contains a stack of ShuffleNet units, one global average pooling, fully connected layer and the output (softmax) layer. The ShuffleNet architecture is shown in Fig. 2 . SqueezeNet is a CNN that comes forward owing to light design of its network. However, it has better performance than AlexNet, it yields fewer 50× than AlexNet in model size [31, 32] . The performance of the proposed model is depicted in the form of Receiver Operation Score and Accuracy metrics are also used to evaluate the networks performance, which are given as follows. . The results of the main 4-class scenario are presented in Table 2. The table compares between the performance of the proposed COVIDetection-Net and the two other models: ShuffleNet and SqueezeNet. The above-mentioned evaluating metrics are the top metrics used to evaluate the performance of classification models. The proposed COVIDetection achieved the highest recall, specificity, precision, F1-score and accuracy of 100% for COVID class. While higher performance is obtained of other classes comparatively to SqueezeNet and shuflleNet models as presented in Table 2 . It is indicated that the performance of the two pneumonia classes (bacterial and viral) are lower than other classes, subsequently lower overall accuracy. This accuracy is significantly increased when combining the two pneumonia classes into one pneumonia class. This can be cleared in 3-class scenario of the proposed, SqueezeNet and shuflleNet models as shown in Table 3 . It is clear from the Net provides the highest detection accuracy of 100% of the COVID19 in the three scenarios (i.e. . Additionally, the average recall, specificity, precision, F1-score and accuracy of the proposed 4-class, 3-class and binary COVIDetection-Net are summarized in Table 5 . To evaluate the performance of the proposed COVIDetection-Net, a comparison study between the proposed model and other models are performed as mentioned before in Tables 2, 3, 4 and Fig. 5 . The results prove the superiority (notability) of the proposed COVIDetection-Net. Moreover, our proposed COVIDetection-Net superior other existing state-of-the-art studies that used CRIs in COVID19 detection as given in Table 6 . The performance values of each study are listed in the table for binary, 3-class and 4-class cases in the term of classification accuracy. Table 7 gives performance comparison study of the proposed COVIDetection-Net with CoroNet and CovXNet on 4-class classification case. The CRIs datasets of the proposed model and the two other compared models are selected from the same databases [27, 28] . The CoroNet model [20] is based on Xpection model with a dropout layer and two fully-connected layers Conflict of interest The authors declare that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Table1 the summary of the prepared dataset. J o u r n a l P r e -p r o o f COVID-19 Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR Essentials for Radiologists on COVID-19: An Update-Radiology Scientific Expert Panel Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT Chest CT findings of COVID-19-infected patients, are there differences between pediatric and adult patients? A systematic review Chest CT findings of early and progressive phase COVID-19 infection from a US patient Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays Truncated inception net: COVID-19 outbreak screening using chest X-rays Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study Detection of coronavirus disease (COVID-19) based on deep features and support vector machine Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks Automated detection of COVID-19 cases using deep neural networks with Xray images A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2 COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images COVID-XPERT: AN AI POWERED POPULATION SCREENING OF COVID-19 CASES USING CHEST RADIOGRAPHY IMAGES CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest Xray images with transferable multi-receptive feature optimization COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based corona detection method using lung X-ray image COVID-19 image data collection Kaggle chest X-ray images (pneumonia) dataset In-line inspection solution for codes on complex backgrounds for the plastic container industry ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size Efficient deep feature selection for remote sensing image recognition with fused deep learning architectures