SHS Web Conf.
Volume 102, 2021The 3rd ETLTC International Conference on Information and Communications Technology (ETLTC2021)
|Number of page(s)||5|
|Section||Applications in Computer Science|
|Published online||03 May 2021|
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
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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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