Swift Diagnose: A High-Performance Shallow Convolutional Neural Network for Rapid and Reliable SARS-COV-2 Induced Pneumonia Detection

Abstract

INTRODUCTION: The SARS-COV-2 pandemic has led to a significant increase in the number of infected individuals and a considerable loss of lives. Identifying SARS-COV-2-induced pneumonia cases promptly is crucial for controlling the virus's spread and improving patient care. In this context, chest X-ray imaging has become an essential tool for detecting pneumonia caused by the novel coronavirus.
 OBJECTIVES: The primary goal of this research is to differentiate between pneumonia cases induced specifically by the SARS-COV-2 virus and other types of pneumonia or healthy cases. This distinction is vital for the effective treatment and isolation of affected patients.
 METHODS: A streamlined stacked Convolutional Neural Network (CNN) architecture was employed for this study. The dataset, meticulously curated from Johns Hopkins University's medical database, comprised 2292 chest X-ray images. This included 542 images of COVID-19-infected cases and 1266 non-COVID cases for the training phase, and 167 COVID-infected images plus 317 non-COVID images for the testing phase. The CNN's performance was assessed against a well-established CNN model to ensure the reliability of the findings.
 RESULTS: The proposed CNN model demonstrated exceptional accuracy, with an overall accuracy rate of 98.96%. In particular, the model achieved a per-class accuracy of 99.405% for detecting SARS-COV-2-infected cases and 98.73% for identifying non-COVID cases. These results indicate the model's significant potential in distinguishing between COVID-19-related pneumonia and other conditions.
 CONCLUSION: The research validates the efficacy of using a specialized CNN architecture for the rapid and precise identification of SARS-COV-2-induced pneumonia from chest X-ray images. The high accuracy rates suggest that this method could be a valuable tool in the ongoing fight against the COVID-19 pandemic, aiding in the swift diagnosis and effective treatment of patients.