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

• Main Authors: Nakamura Miyuka, Wang Jiangkun, Phea Sinchhean, Ben Abdallah Abderazek
• Format: Article
• Language: English
• Published: EDP Sciences 2021-01-01
• Series: SHS Web of Conferences
• Online Access: https://www.shs-conferences.org/articles/shsconf/pdf/2021/13/shsconf_etltc2021_04007.pdf

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.