The COVID-19 pandemic continues to severely undermine the prosperity of the global health system. To combat this pandemic, effective screening techniques for infected patients are indispensable. There is no doubt that the use of chest X-ray images for radiological assessment is one of the essential screening techniques. Some of the early studies revealed that the patient’s chest X-ray images showed abnormalities, which is natural for patients infected with COVID-19. In this paper, we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest x-ray images, named as Parallel-Dilated COVIDNet (PDCOVIDNet). First, the publicly available chest X-ray collection fully preloaded and enhanced, and then classiﬁed by the proposed method. Differing convolution dilation rate in a parallel form demonstrates the proof-of-principle for using PDCOVIDNet to extract radiological features for COVID-19 detection. Accordingly, we have assisted our method with two visualization methods, which are speciﬁcally designed to increase understanding of the key components associated with COVID-19 infection. Both visualization methods compute gradients for a given image category related to feature maps of the last convolutional layer to create a class-discriminative region. In our experiment, we used a total of 2,905 chest X-ray images, comprising three cases (such as COVID-19, normal, and viral pneumonia), and empirical evaluations revealed that the proposed method extracted more signiﬁcant features expeditiously related to suspected disease. The experimental results demonstrate that our proposed method signiﬁcantly improves performance metrics: the accuracy, precision, recall and F1 scores reach 96:58%,96:58%,96:59% and96:58%, respectively, which is comparable or enhanced compared with the state-of-the-art methods. We believe that our contribution can support resistance to COVID-19, and will adopt for COVID-19 screening in AI-based systems. Keywords COVID-19Chest X-rayConvolutional Neural Network Artiﬁcial Intelligence Parallel dilation Dilation rate APREPRINT - JULY 14, 2020 1 Introduction Coronavirus or covid-19 is a contagious disease that was caused by the SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). The disease was ﬁrst discovered and became prevalent in Wuhan, Hubei Province, China, and has since spread around the world. As we know, on March 11, 2020, the World Health Association (WHO) proclaimed the ﬂare-up of coronavirus pandemic [1]. Until now, coronavirus pandemic has devastatingly affected the prosperity of the world economy and health system. As of July 12, 2020, more than 12,401,262 conﬁrmed cases of COVID-19 and 559,047 conﬁrmed deaths due to the disease [2]. Contemporaneous observations detail that COVID-19 infection causes lung illness, hence, making it troublesome to breathe. As well, a person infected with coronavirus spread the virus among others primarily through coughs or sneezes as tiny droplets, therefore, a legitimate social framework for a disengagement is obligatory to delimit the disease. While attempting to deal with growing numbers of COVID-19 infected cases, all nations try to increase testing to reduce the pandemic ahead of time. At present, it is vital to extend an effective screening strategy within reach to distinguish COVID-19 cases and segregate infected individuals from others while hospitals are striving with scaling up abilities to meet the increasing number of patients [3]. An ultimate goal against COVID-19 battle is to provide prompt treatment to infected individuals using a viable screening technique. As we all know, reverse transcriptase-polymerase chain reaction (RT-PCR) is a reliable and well-documented screening technology, which has been accepted by researchers and clinical staff, but the ﬂexibility and strict necessity of conditions at the clinical laboratory will greatly delay the accurate detection of suspicious patients [4]. Moreover, starting the results of RT-PCR testing in China indicated moderately poor sensitivity, and this issue occurred due to the variable characteristics of testing [3]. Further, subsequent revelations illustrated a noteworthy factor that positive rate declined with time after symptom incipience at once relied on how the specimen collected [5]. On the other hand, the limitations of RT-PCR testing have prompted researchers to ﬁnd a rapid and deﬁnitive method for diagnosing COVID-19 infection. Even though COVID-19 pandemic will signiﬁcantly affect the global healthcare system, a radiography testing such as a chest X-ray would be a discretionary screening technique that has as well utilized for COVID-19 diagnosis. Certainly, chest X-ray is beneﬁcial in emergency diagnosis and treatment considering, the way that this system is quick and simple to operate, and radiologists can yet recognize. In the prior observations, researchers observed that patients exhibit inconsistencies in chest X-ray images that are typical for those infected with COVID-19 [6]. However, a few British hospitals are presently turning out artiﬁcial intelligence (AI) based disease recognition systems to look for potential symptoms of Covid-19 infection in chest X-ray images, such as patterns of opacity in the lungs [7]. Signiﬁcant diagnostic accuracy in medical imaging, which is an indispensable objective of automatic disease detection, has already achieved in recent years using CNN, as the core advancement of the rising AI. Inspired by this, recently, a lot of research has already accomplished by utilizing CNN modalities to detect COVID-19 using chest X-ray images, and has obtained promising results [3, 8–11]. The purpose of this research is to extend the curative effect using chest X-ray images showing COVID-19 infection. In this regard, we consider the CNN-based framework as it is renowned for its excellent recognition performance in object classiﬁcation. In CNN, convolution is one of the core operations to regulate the architecture through its dilation rate. The traditional discrete convolution performs the convolution process at a dilation rate of 1, while the dilated convolution [12] uses a different dilation rate to perform the convolution process, and the proposed PDCOVIDNet varies dilation rate in a parallel stack of convolution layer in CNN, thus reﬂecting more distinctive features. In our experimental evaluation, 219 chest X-ray images of COVID-19, 1341 normal, and 1345 viral pneumonia were collected and processed and used to train, validate, and test the proposed method. The benchmark data set is publicly accessible [13], and the authors of this benchmark generated data from three different open access data repositories containing chest X-ray images [14 –16]. The pipeline of PDCOVIDNet starts with the data augmentation strategy, and then optimizes and ﬁne-tunes the settings to train CNN modalities that expand in parallel, through generating dominant features at different scale shifting receptive ﬁelds. Next, the generated features are fused into a neural network system to produce the ﬁnal prediction. Also, we use two gradient-weighted class activation maps (such as Grad-CAM [17] and Grad-CAM++ [18]) to aid our system. These maps provide predictive explanations, and can identify important features related to COVID-19 infection. From experimental evaluation, it shows that the proposed method can identify important features related to COVID-19 disease, and the best accuracy achieved is 96:58%. The contributions of this paper are as follows: We propose and develop a novel CNN framework called PDCOVIDNet to detect COVID-19 from chest X-ray images. Our proposed framework uses a dilated convolution in the parallel stack of convolutional blocks that can capture and propagate important features in parallel over the network which enhances detection accuracy signiﬁcantly. We visualize the X-ray images to analyze the COVID and non-COVID cases, and further investigate the incorrect classiﬁcation. Finally, we empirically evaluate our approach with the state-of-the-art approaches to highlight the effectiveness of PDCOVIDNet in detecting COVID-19. 2 APREPRINT - JULY 14, 2020 We organize the rest of this paper in the following ways. Section 2 reviews the state-of-the-art models used in detecting COVID-19 using chest X-ray images. The benchmark dataset and the augmentation strategy are described in Section 3. Next, Section 4 explains the main details of the proposed model and its adjustment to the detection of COVID-19 cases. In Section 5, we provide the experimental results and show the comparison between PDCOVIDNet and other models. Observation on visualization techniques and incorrect classiﬁcation results are illustrated in Section 6 and Section 7, respectively. Finally, Section 8 provides the conclusion of this paper with the future research direction. 2 Related Works With the rapid spread of COVID-19 in many countries around the world, imaging technology can quickly detect COVID-19, which helps to control the spread of disease. Chest X-ray is a promising imaging technology with a historical prospect of an image diagnosis system. It can be fully explored through various feature extraction methods, thereby playing an important role in the diagnosis of COVID-19 disease. Due to the need for a faster interpretation of chest X-ray images, a CNN-based AI system provides [19] a comprehensive overview of the latest application areas of AI for COVID-19, which mentions medical imaging for diagnosis. Halgurd et al. [20] proposed a customized CNN and pre-trained AlexNet [21] model through chest X-ray images, the results showed very promising accuracy in detecting patients infected either COVID-19 or normal with 98% and 94.1% accuracy respectively. By using the pre-trained ResNet50 [22] model, Narin et al. [8] obtained 98% accuracy. The authors also evaluated several CNN architectures for COVID-19 detection. The Inception [23] transfer learning model modiﬁed to extract radiological features for accurate COVID-19 diagnosis, and then experienced internal and external validation with an accuracy of 89.5% [24]. Also, the CNN architecture based on transfer learning, named as COVID-Net [3], used to classify chest X-ray images into three categories: normal, non-COVID and COVID-19 infections. It showed that COVID-Net achieved the best accuracy by achieving a test accuracy of 93.3%. In [10], transfer learning adopted in state-of-the-art convolutional neural network architectures, results demonstrated the proof-of-principle for using CNN with transfer learning to extract radiological features from small data sets. Biraja et al. [25] investigated the uncertainty in the CNN solution based on Bayesian Convolutional Neural Network (BCNN) to improve the diagnostic performance of COVID-19. Another CNN based on ResNet50 used by Bukhari et al. [26] to prove the usefulness and diagnostic accuracy of patients infected with COVID-19 having accuracy of 98.18%. The authors [27] of proposed a deep learning architecture for COVID-19 detection utilizing their benchmark in which images exhibit COVID-19 disease veriﬁed by radiologists, by ﬁne-tuning four renowned pre-trained convolutional models (ResNet18 [22], ResNet50, SqueezeNet [28], and DenseNet121 [29]), thereby yielded promising results in terms of sensitivity and speciﬁcity. A deep learning framework based on EfﬁcientNet [30] proposed by Eduardo et al. [31]. Contrasted with some popular baseline models (such as VGG16 [32] and ResNet50), the learning parameters have greatly reduced, and the COVID-19 case has a 100% positive prediction. Farooq et al. [33] proposed a method called COVID-ResNet that uses a three-step technique, which includes gradually adjusting the size, looping the learning rate search and discriminating the learning rate, and then ﬁne-tuning the pre-trained ResNet50 architecture to improve model performance. A deep learning model called DarkCovidNet [34] proposed for the automatic detection of COVID-19 using chest X-ray images. The average classiﬁcation accuracy of binary classiﬁcation (such as COVID and No-Findings) was 98.08%, and the average classiﬁcation accuracy of multi-class classiﬁcation (such as COVID, No-Findings and pneumonia) was 87.02%. Finally, the author provided an intuitive explanation and evaluated by expert radiologists. In a case of AI-based COVID-19 diagnostics system, it is obligatory to verify results with expert radiologists. Almost all methods have shown encouraging results. To the best of our knowledge, a few methods are different only if they have veriﬁed by radiologists. Several methods focus only on quantitative analysis, while other methods focus on qualitative analysis using visualization and localization techniques to prove that their analysis can be used for COVID-19 detection, which aids to allow for human-interpretable explanation. In literature reviews, there are a few model and prediction level integration strategies to mitigate variance errors and enhance the performance and generalization of CNN models. Finally, due to the inadequate number of COVID-19 cases, creating ample benchmarks is a major challenge in COVID-19 detection. Considering a small data set, running a large number of iterative CNN architectures can lead to overﬁtting. 3 Data Pre-processing First, we will introduce the benchmark data set and the expansion strategy in detail to aid the training of the proposed model. Next, we will discuss in detail the proposed PDCOVIDNet architecture design method and the training strategy covering the optimal parameter adjustment. Finally, in order to make suspicious disease detection more convincing, we will integrate visualization techniques to highlight key issues with visual markers. 3 APREPRINT - JULY 14, 2020 3.1 Chest X-ray image benchmark The benchmark data set [13] used in our experimental evaluation consists of three main categories (such as COVID-19, Normal, and Viral Pneumonia), yielding 219 COVID-19 positive, 1341 normal and 1345 viral pneumonia chest X-ray images. In the case of accumulating COVID-19 positive images, the authors used two open-access repositories, such as the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 database [14], and the Novel Corona Virus 2019 dataset developed by Joseph et al. [15]. On the other hand, P. Mooney [16] created normal and viral pneumonia images from the chest X-ray image (pneumonia) database used for this benchmark [13]. Moreover, the benchmark is public, and metadata is distributed to provide appropriate document guidance to generate