—Detecting COVID-19 early may help in devising an appropriate t reatment plan and disease containment decisions. In this study, we demonstrate how pre-tr ained deep learning mode ls can be adopted to perform COVID-19 detection using X-Ray images. The aim is to pr ovide over-stressed medica l professionals a second pair of eyes through intelligen t image classification models. W e highlight the challenges (i ncluding dataset size and quality) in utilising current pub licly available COVID-19 datas ets for developing useful deep learning models. We propose a semi-automated image p re-processing model to create a trustworthy image dataset for developing and testing deep learning models. The new approach is aimed to redu ce unwanted noise from X-R ay images so that deep learning models can focus on detecting diseases with specific f eatures from them. Next, we devise a deep learning experimental framework, where we utilise the processed dataset to perform comparative testing for several popular and widely available deep learnin g model families such as VGG, Inception, Xception, and Resnet. The experimental results highlight the suitability of these models for current a vailable dataset and indicate s that models with simpler networks such as VGG19 performs relatively better with up to 83 % precision. This will provide a solid pathway for researchers and practitioners to develop improved models in the future. Keywords —COVID-19 detection, CNN models , X-Ray based detection, image p rocessing, model comparison I. INTRODUCTION The current COVID-19 pandemic has impacted the world with over 2.3 million infections and close to 160,000 deaths so far (as of 19th April 2020). Early identifying , isolation and care for patient s is a key strategy for a better management of this pandemic [1]. Our study aims to provide a co nce ptual f ram ewor k to s upport C OVID- 19 detection with the use of image classification using deep learn ing models. The study will demonstrate how deep learning can be used for COVID-19 detection, which could be use d as a supporting tool for highly constrained health professionals in determin ing the course of treatment. Th e study further develops a pre-processi ng pipeline for improving the image quality, for deep learning-based predic tions. We also provide a comparative study between several pre-trained deep learning models which will pro vide insights for further system development in this application domain. Fast, accessible, affordable and reliable identification of COV ID-19 pathology in an individual is key to slowing the transmission of COVID -19 infection. Currently, reve rse transcriptase quantita tive polymerase chain reaction (RT-qPCR) tests are the gold standard for diagnosing C OVID-19 [2]. During this test small amounts of viral RNA are extracted from a nasal swab, amplified, and quant ified with virus detec tion indicated visually using a fluorescent dye. Unfortu nately, the RT-qPCR test is man ual and time-consuming with results taking up to two days. Some studies have also shown false positive Polyme rase Chain Reaction PCR testing [3]. Other testing approaches include imaging technology-based approaches including computed tomography (CT) imaging [4] and X-Ray imaging based [5][43]. The CT scan-based COVID-19 detection is time consuming and manual with the requirement of expert involvements. Several aut omated approaches have been proposed recently [4, 6, 7]. Both the PCR tests and CT scans are compar atively costly [8, 9] an d with high demand many countries are forced to perform selective testing for only high risk population. X-Ray imaging is relatively cost effective and commonly done fo r lung infection detection and is useful for COVID-19 detection as well [10]. Medical observations were made by one of the co-author of this research (Dr. Saha) who is also a medical pro fessional, as well as by treatin g doctors of the COVID-19 dataset [11] patients. The common features observed in the X-Ray images of patients wi th COVID-19 is a pat chy infiltration or opacities that bear similarities to other viral pneumonia featu res. X-Ray images do not show any abnormalities in the early stages of COVID-19. However, as the disease progr esses, COVID-19 gradually manifests as a typical unilateral patchy infiltration involving mid zone and u pper or lower zone of the lungs, occasionally with evidence of a consolidation. Figure 1 shows the progression of evidence for a patient i n the COVID-19 dataset. (a) 22/01/2020 (b) 25/01/2020 (c) 27/01/2020 (d) 28/01/2020 Figure 1: COVID 19 progression over several days for a sample patient. Computer vision diagnostic tools for COVID-19 from X-Ray images would provide an automated “second reading” to clinicians, assisting in the diagnosis of COVID-19 and informing better decision making in the global fight against the disease. COVID-19 often results in pne umonia, and for radiologists and practitioners differentiating between COVID-19 pneumonia and other types of p neumonia (viral and bacterial) solely on the basis of X-Ray images could be challenging [12]. Deep learning artificial neural n etworks, and in particular the Convolutional Neural Networks (CNNs) have proven to be highly effective in a vast range of medical image classification applications [13, 14]. In this study, we comparatively tested five (5 ) common off-the-shelf pre-train ed CNNs namely VGG16/VGG19 [15], Resnet50 [16], Inception V3 [17] and Xception [18] to determine which CNN implementation is the most effective within the limitation s of the publicly available COVI D-19 X-Ray image samples [11]. The key goals of our experiments with these mode ls is to find the most suitab le deep learning model applicable for the available data for COVID-19 detection from X-Ray images. We aim to apply the models with two distinct scenarios: (a) Identifying pneumonia (both COVID-19 and other types) affected lung against normal lung. (b) Identifying COVID-19 affected lung from non COVID-19 pneumonia affected lung. Noting that the source X-Ray imag e samples, especially those fr om the COVID-19 data set, have been harvested from multiple sources and are of inconsistent quality we have implemented a p re-processing pipeline to reduce unwanted signal noise such as non-lung area visible i n X-Rays, and thereby reduce the impact of sampling bias on this comparison. Through this pre-processing p ipeline, we only identif y lung area and remove rest of the X-Ray image. This would allow models to train on lu ng features only thus hav ing a greater chance of learning disease features and i gnoring other noise features. In the following sections we firs t present a brie f review of re cent scholarly works related to this study, followed by a discussion on the av ailable datasets we used and related challenges. We then present the dataset generation process along with our proposed pre-processing pipel ine for data quality balancing. We then present the deep learning model selection process along with comparativ e results and discussions. II. RELATED WORK Computer aided detection and diagnosis of pulmonary pathologies from X-Ray images is a field of research that started in the 1960s and st eadily progressed in the follow ing decades with papers de scribing highly accurate diagnosis of a range of conditions including osteoporosis [19], breast cancer [20], and cardiac disease [21]. Difficulties in distinguishing sof t tissue caused by poor cont rast in X-Ray images have led some researchers to implement contrast enhancement [22] as a pre-processing step in X-Ray based diagnosis. In addition, lung segmentation of X-Ray images is an important step in the identi fication of lung nodules and various segmentation approaches are proposed in the literature based on linear filtering/thresholding, rolling ball filters and more recently CNNs [23]. In our literature review we not ed a small number of very recent studies that have used deep learning systems for COVID-19 screening and diag nosis. A custom-built 18-layer r esidual network pre-trained on the ImageNet weights against COVID-19 (100 images) and Pneumonia (1431 image s) X-Ray image datas ets [24]. A range of deep learning framework s coined as COVIDX-N et trained on a smal l data set of 25 confirmed COVID-19 cases [25]. A custom curated dataset o f COVID-19, viral pneumonia and normal X-Ray images [26]. A custom residual CNN that was highly effective in distinguishing betwee n COVID-19, Pneumonia and normal condition X-RAY images [27]. These studies u sed the COVID-19 dataset [11] for the COVID-19 X-Ray samples and the RSNA dataset [28] was used to ge t pneumonia and normal X-Ray sa mples. Although some of these studies showed promising results, they have not addressed the variable quality of the COVID-19 data set or minimisation of sampling bias. We also noted that the experimen tal datasets used in the se studies are quite unbalanced. Class imbalance is a common problem in medical imag e data sets that can lead to over- classification of the majority class at the expense of under-cl assification of the minority class [29]. The COVID-19 dataset [11] is probably the only publicly availab le source for some COVID-19 X-Ray images at present. The quality o f these images is highly incons istent since these images were shared from different parts of the world and are often taken in non-ideal c onditions and vastly different equipment. On the other hand, the RSNA dataset [28] showed more uniformity and ap peared to be highly curated. The quality variation between these two datas ets are often readily apparent to the untrained eye as shown in Figure 2. The RSNA dataset also contains a large number of patients who are j uvenile. The COVID-19 dataset however contains mostly adult patients. These discrepancies between the se datasets are mostly ignored by currently available studies. This may have impacted the performance of th e models they developed, since there is a good chance that the deep learning models learned the quality discre pancies between the two datasets rather than characteristics of the diseases. We believe the significant quality variations between data from different classes need to be balanced for deep learning models to learn actual disease related variations . Therefore, our study str esses the importance of sampling bias/signal noise removal from the X-Ray image dataset s prior to using them for model development and classification in order to obt ain meaningful and trustworth y classification results. W e present the dataset creation process in the next section . Landscape orientation Dark Side Margins Rounded/Angled Ribcage (a) Typical sample from RSNA dataset Portrait orientation No prominent margins Straight ribcage (b) Typical Sample from the COVID19 dataset Figure 2 : Visual comparison of typical samples from the RSNA and COVID- 19 datasets. III. DATASET DEVELOPMENT A. Data sourcing Large numbers of X-Ray images are available from a number of pu blicly accessible datasets. With the emergence of COVID-19 being very recent none of these large rep ositories contain any COVID-19 labelled data, thereby requiring that we rely upon at least two datasets for Normal, Pneumonia and COVID-19 source images. COVID-19 chest X-Rays were obt ained from the publicly accessibl e COVID-19 Image Dat a Collection [11]. This collection has been sourced from websites and pdf format p ublications. Unsurprisingly, the images from this collection are of variable size and quality. As of our most recent data that we have downloaded was on 12 Ap ril 2020. There was a total of 115 PA view images tagged as COVID-19 within this data set. The sizes of these images are highly variable with the smallest being 255 x 249 pixels and the largest being 4280 x 35 20 pixels. Image contrast levels, brightness and subject positioning are all highly variable within this dataset . The data set mostly c ontains data for adult patients. Few examples of this variability are shown in Figure 3. (a) Good quality image (b) Squared off image (c) Abnormally long focal distance (d) Washed out – high brightness/low contrast (e) Incomplete projection. Intrusive medical appliances (F) Image with markings Figure 3: Different variations observe d in the COVID-19 dataset. National Institute of Health (NIH) Chest X-Ray [30] – a source of 112,120 anonymized labelled X-Ray images with 14 condition labels including Pneumonia and normal conditions. The images a re of a similar size, [31] quality and aspect ratio to the typical images in the COVI D-19 dataset with dimen sions being uniformly 1024 x 1024 pixels in a portrait orientation. A comparison of i mages from the NIH dataset to the COVID-19 dataset is shown in Figure 4. (a) Typical sample from NIH dataset (b) Typical Sample from the COVID19 dataset Figure 4 : Visual comparison of samples f rom the NIH and COVID-19 datase ts Visually, the levels of intra dat aset difference between images are similar. While the differences between the samples from two datasets are also not strongly apparent as it was evident between RSNA and COVID-19 datasets in Figure 2. We consequently chose this dataset as the source for pneumonia and normal condition X- Ray images. Our findings in relation to ava ilable data sources is summarise d in Table 1: TABLE 1: SUMMARY OF DATA SOURCES CONSIDERED Collection Number of Image s Characteristics Notes COVID-19 Image Data Collection [11] 115 COVID-19 (PA) Variable size, quality, contrast and brightness. Only source of COVID-19 PA X-Ray images and used in this study. Montgomery County X-Ray Set[32] 80 Normal 58 Tuberculosis N/A Not used due to too few normal images and no pneumonia labelled images Kaggle RSNA Pneumonia Detection Challenge [28] 8851 Normal 9555 Lung Opacity 11821 No Lung Opacity/Not Normal Very uniform. PA view differs to typical COVID-19. Clear systematic visual differences to the COVID-19 collection, hence not used in this study NIH Chest X-Ray[30] 322 Pneumonia 60361 No Finding Uniformity similar to COVID- 19 dataset. All images are 1024 x1024 in size. Objectively similar in quality to the COVID-19 Image Data Collection. Used in this study. B. Data Sampling In this study we aim to use real X-Ray data only and not consid ering creation and use of synthetic data at this stage. We also planned to us e balanced dataset size for ou r model experiments. From the two source datasets COVID-19 and NIH, a mas ter dataset containing COVID-19 , Pneumonia and Normal images was created for our model development and testing purposes. The COV ID-19 dataset was curated to remove images that were the wrong projection, poorly cropped, low resolution or where medical dev ices were a dominant feature (since these might draw the attention of the machine le arning algorithms). This left us with 100 usable samples for COVID-19 dataset. Sin ce the NIH dataset contains mu ch more samples, we used down sampling technique to select matching numbers of samples for Normal and Pneumonia cases. The Pneumonia and Normal samples were selected from the do wnloaded NIH dat aset randomly and excludes samples for juvenile patients. A small number of images selected in this way were found to con tain medical devices (pacemakers) which we were concerned might draw the attention of the machine learning algorithms, so these images were discarded and replaced with a random selec tion until such d evices were ab sent from the data set. The results of this procedure are summarised in Table 2. TABLE 2: SAMPLED DATASET FOR EXPERIMENTS Source Condition Number of source images Number of curated images Curation notes COVID-19 Image Data Collection COVID-19 115 100 Incorrectly labelled sample excluded Low quality images (low pixel count, poorly cropped etc.) excluded Samples with medical devices excluded NIH Chest X-Ray Pneumonia 322 100 100 samples chosen randomly Juvenile patients excluded Samples with medical devices NIH Chest X-Ray Normal 60361 200 200 samples chosen randomly Juvenile patients excluded Samples with medical devices C. Data Pre-Processing 1) Equalization of Sampling Bias We recognised that although we were using only two different da tasets (COVID-19 and NIH) for our experiments; we were in fact relying upon data sourced from an unknown number and variety of X-Ray machines, exposure parameters and operator behaviour. Systemati c image exposure and brightness differences for the datasets proved to be pa rticularly concerning and sever al researchers have indi cated that medical image analysis methods are highly sens itive to variati ons of image in tensities [33] and, [34]. Research has shown that the feasibility of an automated a nalysis system requires that “ the intensity of the same tissue should always be similar in one image and not vary with the location of the tiss ue”[34]. This principle when extrapolated to the many images utilised in machine learning algorithms, implies th at all images in the sample data sets should have similar intensity for the same tissue over the entire set of images as well as within a single image. Since the machine learning classi fiers use a pixel array as a d ata source then any systematic difference in pixel intensity between the datasets would introduce sampling b ias in the result. This would have the consequence of training the mach ine learning classifiers on sys tematic image histogram differences rather than the actual clinical image content of interest. To minimize the effect of sampli ng bias, we applied histogram e qualization to images using the OpenCV equalizeHist [35] function. As shown in Figure 5, the equalizat ion process greatly improved the image brightness/contrast consistency across datasets. The same effec t was observed within each data set. Subjectively, the authors can n o longer easily tell which image has been drawn from w hich dataset purely on image brightness and contrast characteristics alone. (a) COVID-19 original (b) COVID-19 equalized (c) COVID-19 equalized and segmented (d) Pneumonia original (e) Pneumonia equalized (f) Pneumonia equalized and segmented (g) Nomal original (h) Nomal equalized (i) Nomal equalized and segmented Figure 5: Results of equalization and se gmentation on original samples f or COVID-19, Pneumonia and Normal images. 2) Segmentation based noise reduction Since the COVID-19 and Pneumonia affects the lung, we wanted to improve machine learning classification attention to the lung region of interest only. The aim is to re move the noise data such as collar bone, neckline, border, diaphragm etc. around th e lung area. The natural way to achieve this was by cropping all but the lung area from the images sent to t he classifier. The efficacy of th is approach in medical image classification is supported in past studies [36]. A comprehensive review of lung area segmentation techniques may be found in [37]. This study notes that intra-thoracic pathology, external objects such as jewellery and even subject position and posture all adversely affect the current lung segmentation algorithms and the probl em of accurate lung segmentation from PA X-Rays is an open problem. Our data sets show these problematic characteristics, and perfe ct automatic lung segmentation was therefore unlikely regardless of the technique applied. We applied the Op enCV GrabCut [38] function, which was previously used for CT scans [39] as a simple tool implement se gmentation technique on o ur equalized images. We reasoned that the lung area co uld be considered the foregrou nd of the X-Ray image. The segmented results for different image classes are shown in Figure 5. 3) Pre-processing Pipeline Firstly, the pre-processing pipe line shown in Fig ure 6 is respo nsible for applying histogram equalization and the GrabCut algorithm to the sam ple images. Images are first r ead before being resized to 640 x 640 pixels. This initial resize is performed i n order to optimize the perfo rmance of the downstream GrabCut process. We found by experimentation that GrabCut worked much better on the reversed image compared to the original image. Following the applicati on of GrabCut, the image again r eversed (back to normal) to complete the pre- process. The detailed pre-processing pip eline is shown in figure 6. Figure 6: Data pre-processing pipeline. IV. MODEL DEVELOPMENT A. Classification Pipeline The basic classification pipeline is shown in Figure 7, which r eads in images sorted into subdirectories with subdirectory name used as the ima ge classes. The images are res ized into 224 x 224 pixels array before being randomly split into 80:20 trainin g and testing data subsets. Im age data was augmented only with a rotation operation with a range of 15. Each tested model is then trained over 20+ epochs with precisio n, recall, training and testing accuracy and loss metrics are captured for further analysis. Figure 7: Classification pipeline. Read imageResize to 640px x 640pxConvert to GRayEqualize histogramReverse image Convert to colorApply GrabCut Reverse image Read images/labelsResize to 224px x 224px80:20 split Augment dataTrain model on 80% Test model on 20% B. Model Consideration One of the key objectives of this study was to achieve state-of -the-art classification results using publicly accessible data and “out-of-the-box” models with transfer learn ing to both compensate for the limited size of the sample data, and to accelerate th e training process so that thi s could be reasonably performed on modest hardware. For these reasons we have select ed five deep learning models as classifiers for our experiments. Conveniently, these models are a ll available as part of the Ker as API and each support t ransfer learning [44] in the form of supporting the pre-application to the model of the ImageNet [40] weights. 1) VGG16 and VGG19 VGG16 and VGG19 [15] are convolutional neural network (CNN) arc hitectures with very small convolution filters (3x3) and a str ide of 1 designed to achieve high accura cy in large-scale image recognition applications. The two implementations differ in depth of convolution/max-pool ing and fully connected layers, with VGG16 having 16 layers in the base model and VGG19 having 19 layers. 2) ResNet50 The ResNet [16] CNN was developed as a means of avoiding the va nishing gradient problem inherent in deep neural networks by implementing a system of skip connectio ns between layers – known as residual learning. This architecture resu lts in a network that is more efficient to train, allowing for deeper networks to be designed that positively impact the model accuracy. ResNet50 is such a network with 50 layers implementing residual learning. 3) Inception V3 The Inception V3 [17] CNN aimed to improve utilization of compu ting resources inside the network by increasing the depth and width of the network whilst keeping co mputation operations constant. The designers of this network coined the term “inception modules” to describe an optimised network structure with skipped connections that is used as a building block. This inception mo dule is repeated spatia lly by stacking with occasional max-pooling layers t o reduce dimensionality to a man ageable level for computation. 4) Xception The Xception [18] CNN was developed by Google Inc. as an “extr eme” version of the Inception model. The Inception modules described above are replaced with depth w ise separable convolutions. This Xception was shown to outperform Inception on a large-scale image classifica tion dataset (comprising 350 million images of 17,000 classes). V. MODEL ASSESSMENT A. Computing Infrastructure All the experiments were performed using an off-the-shelf Lenov o P50 laptop with an Intel Core i7-6820HQ CPU @ 2.70GHz 4 Core CPU. The laptop hosts an NVIDIA Quadro M1 000M GPU and an Intel HD Graphics 530 on-board graphics card. There was 32GB physical RAM instal led. B. Experiment Setup To ensure a consistent compari son across the models, each model was instantiated with a uniform output head as shown in Figure 8: Figure 8: Head architecture of the proposed models. Each experiment was performed 20 times with using 20 Epochs, Le arning Rate = 1e-3 and a Batch Size of 8. C. Experiment Dataset The master dataset was utilised f or training and validation wit h each candidate classifier over 2 experiments per classifier as shown in Table 3. Processed dataset is available for public use at https://github.com/mhorry/SegmentedLungCXRs TABLE 3: DATASETS USED FOR EXPERIMENTS Experiment ID Experiment Dataset Experiment 1 Normal vs COVID-19 and Pneumonia. 200 x Normal vs 100 x COVID-19 100 x Pneumonia Experiment 2 COVID-19 vs Pneumonia 100 x Pneumonia Vs 100 x COVID-19 D. Comparative Results and Discussions The experimental results are listed in Table 4. For each class ifier we have noted average Precision (P), Recall (R) and F1 score over the 20 experimental runs. Experiment 1 classified normal condition against a combined dat aset of COVID-19 and Pneumonia. Experiment 2 classified COVID-19 vs Pneumonia. The numbers provided in the Tabl e 4 shows averages over 20 test cycles as percentages (%). TABLE 4: PERFORMANCE RESULTS FOR TESTED MODELS Experiment VGG16 VGG19 ResNet50 InceptionV3 Xception P R F1 P R F1 P R F1 P R F1 P R F1 Experiment 1: Normal vs. (COVID-19+ Pneumonia) 82 80 80 83 80 80 70 67 66 68 65 61 69 57 48 Experiment 2: COVID-19 vs, Pneumonia 83 81 80 83 81 81 67 58 51 41 50 36 30 50 34 To understand these results it is useful to consider the learni ng curves for experiment 1 as shown in Figure 9. Base Model (VGG16/19, ResNet50, Inception, Xception)Average Pooling 2 x 2FlattenFully Connected (Relu) Dimension = 64Dropout = 0.5OUTPUT: Fully Connected (Softmax) Dimension = 2 (a) VGG16 (b) VGG19 (c) ResNet50 (d) Inception V3 (e) Xception Figure 9: Learning curves for different models. One interesting observation is t hat the deeper n etworks, being ResNet50, Inception an d Xception exhibited poor performance compared to the shallower networks being VGG16 and VGG19. Noting th at our sample sizes were small (200 samples for each class in Experiment 1 and 100 samples per class in Experiment 2 and the challenges inherent in training C NNs with small data sets as de scribed by D’Souza et al. [41]. We suspect that we did not have enough X-Ray sam ples to train these deeper netw orks. Given the currently limited number of quality samples of COVID-19 X-Ray images, we believe that the V GG16 or VGG19 classifiers to be the most accurate off-the-shelf for classif ication of COVID -19 and Pneum onia from Normal condition, and for COVID- 19 vs Pneumonia. Both VGG16 and VGG19 performed very well in Ex periment 1 and both were successful in distinguishing between normal and COVID-19 or Pneumonia patholo gies with an average F1 score of 80%. VGG19 slightly outperformed VGG1 6 in experiment 2 with a 1% imp rovement in F1 score for experiment 2 following an additional 20 traini ng cycles indicated by the ori ginal VGG19 training curve for experiment 2. As additional high quality COVID-19 X-Ray image sample become a vailable we expect that the VGG-19 classifier to generally outperform the VGG-16 classifier. For this reason, we recommen d the VGG19 classifier over the VGG16 classifier. VI. CONCLUSIONS We have demonstrated that deep learning models for the automate d identification of COVID-19 to be feasible using publicly available X-Ray image sample training/v alidation data and off-the-shelf deep learning algorithms. We provided a pre-processing pipeline aimed to remove the sampl ing bias. Our results of around 80% for both recall and precision are reas onable firstly, given our sma ll, and variable quality, COVID-19 X-Ray image sample size, and secondly that trained clinicians have an inter pretative radiological error of 2 – 20% depending on the radiological investigation [42]. We found that both VGG16 and VGG19 classifiers provided good re sults within the experimental constraints of the small number of currently available COVID-19 X-Ray image samples. As the number of available COVID-19 X-Ray image s amples increases we will be abl e to provide enough training data corpus to the deeper networks (ResNet50, Inception, and Xception) and the reby obtain better results f rom these classifiers. This will be the subject of our future work. Despite our good results, we would urge great caution in the de velopment of clinical diagnostic models using currently available COVID-19 X-Ray image data. The effect of a false positive di agnosis of COVID-19 on an individual is the isolation of the individual and their c ontract traces and the mental anguish and stress caused by both the prognosis and the social isola tion. A false positive COVID-19 diagnosis could result in an inappropriate course of treatme nt. The effects of a false negat ive COVID-19 diagnosis would also be devastating for the individual if that diagnosis led to an inap propriate course of treatment, and the community since cautions against COVID-19 transmission may not be appropr iately applied resulting in the further spread of the disease. As a higher quality corpus of COV ID-19 X-Ray image data becomes available, it may be possible to produce deep learning based clinical models for the fast diagno sis of COVID-19 as distinguished from similar conditions such as Pneumonia. Suc h a tool would prove invaluab le in practice, where oth er diagnostic tests for COVID-19 are either unavailable o r unreliable. As the COVID-19 spread progresses throughout remote and economically challenged locations an ability to diagnose COVID- 19 from a readily available chest X-RAY image would help slow the spread of the disease and result in a better medical outcome for the population. ACKNOWLEDGEMEN T We wish to thank IBM Australia f or providing work time release for the 1st author to perform modelling and experimentation work and to contribute to the writing of this p aper.