Deep Learning Approach Using 3D-ImpCNN Classification for Coronavirus Disease

ABSTRACT

Coronavirus (COVID-19) is a disease which is spreading rapidly, and nearly 1,436,000 people have been infected in about 200 countries all over the world as of April 2020. It is essential to detect COVID-19 at the earliest stage to care for the infected patients and, moreover, to prevent spreading and protect uninfected people. Deep learning approach, namely, convolutional neural networks (CNNs), requires extensive training data. Due to the recent epidemic, collecting enormous radiographic images in a very short duration is a challenging task. The major issues toward the success of CNN approach is the smaller dataset. Training dataset is scaled, and the results of detecting COVID-19 are boosted by using the proposed 3D-ImpCNN approach. This paper introduces 3D_ImpCNN classification model to categorize the patient affected by COVID. The COVID-19 classification outcomes of the method introduced is analyzed which produced better results when compared against existing methods. Accuracy of 3D-ImpCNN classification method was 96.5%, and moreover this method assists in detecting COVID-19 in a rapid manner. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.