COVID-19 detection from chest X-Ray images using ensemble of CNN models Sagar Deep Deb Dept. of Electrical Engineering Indian Institute of Technology Patna Patna, India sagardeepdeb@gmail.com Rajib Kumar Jha Dept. of Electrical Engineering Indian Institute of Technology Patna Patna, India jharajib@iitp.ac.in Abstract—The year 2020 will certainly be remembered for the outbreak of COVID-19 pandemic. With the first case being reported in December 2019, the SARSCoV2 virus has proved to be one of the most deadly virus which has affected the human civilization. Relatively high contagious rate and asymptomatic patients also being carrier of the virus makes it more dangerous. The only way to get a control on the outbreak is rapid testing. With the present testing mechanisms being costly and time consuming the end of this pandemic doesn’t seems near. These challenges motivates us to come up with a system which can be effective in testing large population size and at the same time be less time consuming. We have proposed a Deep Convotuional Neural Network based ensemble architecture for extracting features from Chest X-Ray images and later classifying them into three categories namely- Community Acquired Pneumonia(CAP), Normal and COVID-19. We have shown that applying such tech- nique can give better performance. Our ensemble network uses three pre-trained DCNN networks namely- NASNet, MobileNet and DenseNet. The low level features extracted from the three DCNN architectures are later concatenated and applied to a classifier for final classification. We have achieved an accuracy of 91.99% which is slightly better than the state of the art performances. Index Terms—COVID-19, Chest X-Ray, DCNN, ensemble model I. I NTRODUCTION The first case of COVID-19 was detected in the Wuhan province of China back in December 2019 and started spread- ing all across the world since then. The World Health Orga- nization or WHO has declared it a pandemic in 11th March 2020. Today on October the 12th we have 7.18 million cases and 110,135 people have succumbed to the deadly virus world wide. The spread of this disease seems uncontrollable even after different part of the world had been under harsh lock down at different point of time. The R0 value is a parameter which measures how contagious an infectious disease is and thus it is one of the important scalar to study how the contagious disease has progressed. R0 can be used to find out the transmission and decline rate of that particular disease. Data suggests that the R0 value which changes with geogra- phy, population density and other important social measures followed in a community is higher than the deadly virus outbreak of 1918. The rapid spread of a disease of this kind © IEEE 2021. This article is free to access and download, along with rights for full text and data mining, re-use and analysis. demands a highly accurate point-of-care COVID-19 screening [1]. The gold standard screening approach based on Reverse Transcription Polymerase Chain Reaction (RT-PCR) shows good accuracy but is subject to considerable cost and slow turnover time constraints, rendering it not scalable to the ever- increasing population at risk. [2]. Though the countries are giving most importance to testing but again the large scale testing is somewhat impossible with the given high cost and non scalable testing equipment. Researchers in the field of bio-medical image processing and Artificial Intelligence have tried to find a solution with the available resources. [3]–[5] have displayed great potential with Computed Tomography (CT) images for detection of COVID- 19. The wide availability of Chest X-Ray (CXR) in diverse health care settings makes it an attractive option for rapid, accurate yet inexpensive point-of-care screening in primary care clinics. II. RELATED WORKS Convolutional Neural Networks(CNN) has time and again proved to be the best machine learning algorithm for image classification and recognition task. For almost all bio-medical image classification problems researchers have used CNN. Like for lung cancer [7], blood cancer [8] and breast cancer [9] researchers have reported state of the art performance using CNN structures. Detection and classification of COVID- 19 from both Chest X-Ray and CT images being an image classification task, also attracted researchers to train deep learning models on the largely public datasets available. Li et. al. [1] presented COVID-MobileXpert, a DCNN based mobile application that can used for point-of-care COVID-19 screen- ing from given noisy snapshots of chest X-ray image. Goodwin et. al. [10] have used twelve most common pre-trained DCNN structures to compare the results obtained for classification of Chest X-Ray images into healthy, Community acquired pneu- monia(CAP) and COVID-19. The highest accuracy obtained by them is 88.4% using Densenet201 architecture. In [11] the researchers have used inception migration-learning model to detect COVID-19 from 453 CT images and achieved an accuracy of 73.1%. [12] have also used Chest CT images for detection of COVID-19. They have reported an AUC of 0.96. 2020 International Conference on Power, Instrumentation, Control and Computing (PICC) 2020 International Conference on Power, Instrumentation, Control and Computing (PICC) | 978-1-7281-7590-4/20/$31.00 ©2020 IEEE | DOI: 10.1109/PICC51425.2020.9362499