An Efficient Approach for Automatic detection of COVID-19 using Transfer Learning from Chest X-Ray Images

The coronavirus disease 2019 (covid 19), which was declared a pandemic by the World Health Organization (WHO) in December, causes significant alveolar damage and progressive respiratory failure, resulting in death. The only laboratory technique available, RT-PCR, has an accuracy of about 73 percent. Medical specialists may benefit from early detection using CXR. Using deep convolutional neural network architecture, we propose a Com-puter Aided Diagnosis (CADx) for the diagnosis of coronavirus disease 2019.The chest x-ray dataset is used for testing and training of neural networks. The CXR images are segmented using a U net model, and the segmented image is then used to train a classification model using the Inception v3 model, which distinguishes covid 19 from pneumococcal records and safe records. Training of inception v3 is done with different resolutions of Chest X-rays (CXR) and for further optimization adam optimizer is used. This model produces high computational efficiency with an accuracy of 0.97 per-cent. Based on the promising results obtained the proposed method can be used for effective diagnosis of covid 19 during this pandemic.