COVID‐19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID‐19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID‐19 can be diagnosed with the aid of chest X‐ray images. To combat the COVID‐19 outbreak, developing a deep learning (DL) based model for automated COVID‐19 diagnosis on chest X‐ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID‐19 from chest X‐ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID‐19 and normal chest X‐rays. It provides COVID‐19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross‐dataset studies are used to assess the robustness in a real‐world context. Six hundred X‐ray images were used for training and two hundred X‐rays were used for validation of the model. The X‐ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1‐score. The cross‐dataset studies also demonstrate the resilience of deep learning algorithms in a real‐world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high‐performance of our proposed DL‐based customized model identifies COVID‐19 patients quickly, which is helpful in controlling the COVID‐19 outbreak.
Acknowledgments: The authors are grateful for the support of Taif University Researchers Sup\u2010 porting Project number (TURSP\u20102020/211), Taif University, Taif, Saudi Arabia.
