testing kits, hospital beds and ICU beds, ventilators, personal protective equipment (PPE), the healthcare systems around most of the globe are loaded at the havoc and bound to make selective decisions in terms of testing, patient admission, ICU beds as well as the provision of ventilators. Many countries suffer from false results due to multiple plausible causes such as specimen handling, stage of disease when the specimen is collected, and quality of the specimen. Furthermore, it is laborious, relatively expensive, and has a low sensitivity (60%вЂ“70%). Added with the rapid spread and hence an increased number of specimens collected, the laboratories very rapidly get overwhelmed. One of the major drawbacks of this method is the tedium involved and the time required as the fastest turn-around time is at least 24 hours. Thus, abiding by preventive measures such as social distancing, hygiene maintenance, and contact tracing and enabling a system that can diagnose the disease earlier and faster is paramount.ДЄt present, the eminent standard for diagnosing COVID-19 is the reverse transcription-polymerase chain reaction (RT-PCR) which identifies the nucleotides of the virus from specimens extracted from a nasal swab or oropharyngeal swab. Along with being a highly contagious disease, COVID-19 has a long incubation period, on average, five to six days between the contact and symptom onset phases. This makes detection and containment of the virus even harder. On the other hand, many individuals do not show any symptoms despite being carriers of the virus. Elderly people or people exhibiting comorbidity having a compromised immune system are highly prone to infection and severity. In more severe or progressed cases, pneumonia, development of fluid in the lungs, acute respiratory distress syndrome (ARDS), multiorgan failure, septic shock as well as death may occur. The common clinical characteristics of COVID-19 include a range of symptoms mutual with other viral diseases such as the common cold.

As of 28 June 2020, the total number of global cases and deaths exceed 9.8 million and 4.9 lakh respectively. Despite the mortality rates of these epidemics being much higher than that of COVID-19 (10% for SARS and 30-35% for MERS), the cumulative number of deaths for the latter has surpassed that of both the epidemics combined by many folds. Among them, two had caused epidemics in the last two decades named SARS-CoV and MERS-CoV. Until SARS-CoV-2 surfaced, six types of coronaviruses were known to be able to harm humans by mainly targeting the respiratory system. The virus responsible for the disease, named SARS-CoV-2, belongs to a family of coronaviruses that are zoonotic in nature. Therefore, our proposed approach can be adapted as a reliable method for faster and accurate COVID-19 affected case detection.Д¬OVID-19, the pandemic that has brought the world to a halt, was reported in Wuhan, China in the December of 2019 for the first time, when patients with cases of unidentified pneumonia emerged. Most importantly, the model has shown a significantly good performance over the current research-based methods in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Recall = 1.00, F1-score = 1.00 and Specificity = 1.00). The implemented neural network demonstrates significant performance in classifying the cases with an overall accuracy of 96%.

Our dataset consists of 2905 chest X-ray images of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). In this paper, implementation of a deep transfer learning-based framework using a pre-trained network (ResNet-50) for detecting COVID-19 from the chest X-rays was done. A significant step of COVID-19 affected patientвЂ™s treatment is the faster and accurate detection of the disease which is the motivation of this study. World economy as well as public health have been facing a devastating effect caused by the disease termed as Coronavirus (COVID-19).

Normal chest xray image