Detecting COVID-19 from Chest X-rays Images using Modified Inception Deep Learning Model

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Abstract

The novel Coronavirus (COVID-19) had outbroken in the entire world and caused a pandemic situation. This disease affects the human respiratory system and force to death. The Chest X-ray is one of the essential tools that will suggest detecting infected COVID-19 patients at any stage. The death ratio can be reduced if COVID-19 can be estimated at early stage. Artificial Intelligence (AI) and Deep learning (DL) can be used for this purpose. Hence, this paper applies the modified Inception-Net DL model for detecting coronavirus using the publicly available Chest X-rays datasets. The proposed model performs augmentation, feature extraction, and disease classification. In this study, two public Chest X-ray datasets were utilized, gathered from different patients with COVID-19 and common Pneumonia. The proposed model has been tested on these datasets and found overall accuracy as 99.36% and 98.0% for CoVira-Loc dataset and CovNorm-Loc dataset respectively.

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Keywords

COVID-19, Chest X-ray, Artificial Intelligence, Deep Learning, CNN, Inception Module, Classification.