Convolutional Neural Network Approach in Covid-19 Screening in Asymptomatic Individuals Mohammed-Amine Zyad Polydisciplinary Faculty Sultan Moulay Slimane University Beni Mellal, Morocco zyad@usms.ma Belaid Bouikhalene Polydisciplinary Faculty Sultan Moulay Slimane University Beni Mellal, Morocco B.BOUIKHALENE@usms.ma Abdelmajid Zyad Faculty of Science and Techniques Sultan Moulay Slimane University Beni Mellal, Morocco A.ZYAD@usms.ma Abstract—The new coronavirus (COVID-19) spread rapidly around the globe and hits around six million of cases as reported by the World Health Organization (WHO) at the time of writing. This growth in number of cases made it hard for doctors and radiologists to correctly diagnose the COVID-19 disease. The use of automated methods can be helpful to efficiently and accurately detect the disease, and can also be used to assist doctors who lack the proper tools for diagnosis. In the present study, we provide a novel method using Convolutional Neural Networks (CNN) to accurately classify COVID-19 cases from raw chest X-ray images by 95%. The proposed model is lightweight and can be easily deployed to the Cloud or mobile devices with little compute power. Index Terms—classification, convolutional neural networks, COVID-19, coronavirus I. I NTRODUCTION In December 2019, a new coronavirus disease (COVID-19) was discovered in Wuhan, China [1]. The virus that caused it is known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus has the capacity to transfer from animals to humans due to its zoonotic nature [2]. The symptoms of this disease are divided into two groups, as Zhang [3] stated: ”Those with gastrointestinal symptoms (G group) and those without gastrointestinal symptoms (NG group). Common gastrointestinal symptoms included inappetence, di- arrhea, nausea, abdominal pain, and vomiting. Significantly higher proportions of patients with fever, dizziness, myalgia, and fatigue were noted in group G than in group NG.” The coronavirus started originally in China and spread to Japan, South Korea and Europe after that, Italy and Spain in particular, where it hit very hard [4]. In Morocco the first known case traced back to 2 nd of March 2020 and it went up to 5856 cases in 11 th of May 2020. According to World Health Organization (WHO), the total confirmed cases in the world is around 6.2 million. Doctors and radiologists’ important task is to diagnose cases, whether they have COVID-19 or not, to a certain accu- racy. The problem is that it is easy to get overloaded looking at the huge number of cases. This can lead us to think about automatic methods, mainly machine learning methods which gained a huge success in recent years to assist radiologists in this mission. In the present study, we propose an automatic method using Convolutional Neural Networks (CNN) to detect and classify X-ray chest images wether the patient suffers from COVID-19 or not. In Section III, we will present a brief history about the neural networks and their usage in the medical imaging field and present some alternative methods using automatic methods to classify COVID-19. In Section IV, we present the dataset and the model used to achieve the results cited in this paper. Finally, in Section V we present the results after successfully applying the model on the dataset and an interpretation of what those results mean, besides a limitation to this method. II. DIAGNOSTIC APPROACHES OF SARS-COV-2 The demonstration of the positivity of patients to Covid19 is carried out by a molecular biology test (PCR) and a serological test looking for antibodies (IgM and IgG) against the SARSCoV2 virus. A. Evaluation by a Quantitative RT-PCR Assay Generally, serial samples (throat swabs or sputum) from patients were collected and tested for SARS-CoV-2 [5] [6]. The presence of SARS-CoV-2 in respiratory specimens was detected by quantitative real-time RT-PCR of SARS-CoV-2 [5]. The Total RNA was extracted using the respiratory sample RNA isolation kit. Then, vortex and transfer of 50 mL of cell lysates into another collection tube were made. After that, the tubes were centrifuged at 1000 rpm for 5 min. After 10 min incubation at room, 5 mL of RNA sample was prepared to be used for RT-PCR amplification. Real time RT-PCR was performed using the nucleic acid testing kit specific for SARS- CoV-2 detection. The open reading frame 1 ab (ORF1ab) and nucleocapsid protein (N) were simultaneously selected as the two target genes. The human GAPDH gene was used as an internal control [7]. The specific primers and probes set for ORF1ab and N were as follows: ORF1ab-forward primer 50- ACCTTCTCTTGCCACTGTAGC-30, ORF1ab-reverse primer 50-AGTATCAACCATATCCAACCATGTC-30, probe 50- FAM ACGCATCACCCAACTAGCAGGCATAT- BHQ1-30, N-forward primer 50-TTCAAGAAATTCAACTCCAG- 3, N-reverse primer 50-AGCAGCAAAGCAAG 978-1-6654-4103-2/21/$31.00 ©2021 IEEE 2021 7th International Conference on Optimization and Applications (ICOA) | 978-1-6654-4103-2/20/$31.00 ©2021 IEEE | DOI: 10.1109/ICOA51614.2021.9442641