| Summary: | Coronavirus (COVID-19) disease has not only become a pandemic but also an overwhelming strain on the healthcare industry. The conventional diagnostic methods include Antigen Rapid Kits and Reverse Transcription–Polymerase Chain Reaction (RT-PCR) tests. However, they entail several drawbacks such as low precision in diagnosis, increased time in obtaining test results, increased human–patient interaction, and high inaccuracy in the diagnosis of asymptomatic individuals, thus posing a significant challenge in today’s medical practice in curbing an extremely infectious disease such as COVID-19. To overcome these shortcomings, a machine learning (ML) approach was proposed to aid clinicians in more accurate and precise infection diagnoses. A Convolutional Neural Network was built using a sample size of 1920 chest X-rays (CXR) of healthy individuals and COVID-19-infected patients. The developed CNN’s performance was further cross-checked using the clinical results of the validation dataset comprising 300 CXRs. By converting the final output to binary, an intuitive classification of whether a specific CXR is of a healthy or a COVID-infected patient was accomplished. The statistical analysis of the CNN was: Accuracy: 95%; Precision: 96%; Specificity: 95%; Recall: 95%, and F1 score: 95%, thus, proving it to be a promising diagnostic tool in comparison to the other existing ML-based models. The datasets were obtained from Kaggle, GitHub, and European Institute for Biomedical Imaging Research repositories. The prospects of the proposed CNN lie in its flexibility to be altered and extrapolated in diagnosing other lung infections, such as pneumonia and bacterial infections, with relevant training algorithms and inputs. Additionally, the usage of other bio-imaging modalities as input datasets such as CT scans, Lung Ultrasounds and Heat Maps gives the CNN immense potential to assess for better insights on the severity of infection in both infected and asymptomatic patients as well as other related medical diagnoses.
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