Comprehensive Study of Coronavirus Disease 2019 (COVID-19) Classification based on Deep Convolution Neural Networks

University of Aizu, Graduate School of Computer Science and Engineering, Adaptive Systems Laboratory, Aizu-Wakamatsu, Japan

^* e-mail: m5241120@u-aizu.ac.jp
^** e-mail: d8222108@u-aizu.ac.jp
^*** e-mail: s1250250@u-aizu.ac.jp

Abstract

Artificial Intelligence (AI) has recently become a topic of study in different applications, including healthcare, in which timely detection of anomalies can play a vital role in patients health monitoring. The coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, colloquially known as the Coronavirus, disrupts large parts of the world. The standard way to test for COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR), which uses collected samples from the patient. This paper presents an efficient convolution neural network software implementation for COVID-19 and other pneumonia disease detection targeted for an AI-enabled smart biomedical diagnosis system (AIRBiS). From the evaluation results, we found that the classification accuracy of the abnormal (COVID-19 and pneumonia) test dataset is over 97.18%. On the other hand, the accuracy of the normal is no more than 71.37%. We discussed the possible problems and proposals for further optimization.