🤖 AI Summary
To address the clinical challenge of accurately differentiating COVID-19 from other pulmonary conditions—viral pneumonia, bacterial pneumonia, and normal cases—in chest X-ray images, this paper proposes a purpose-built, 19-layer lightweight CNN architecture. The model performs end-to-end four-class classification and achieves superior performance over mainstream pretrained models—including AlexNet, VGG19, SqueezeNet, Inception-v3, and ResNet50—on public chest X-ray datasets. It attains state-of-the-art results in key diagnostic metrics: specificity, sensitivity, and F1-score. The contributions are threefold: (i) a medical imaging–optimized lightweight architecture that balances diagnostic accuracy with computational efficiency for clinical deployment; (ii) flexible support for both three-class (excluding normal) and four-class fine-grained classification, enhancing clinical interpretability; and (iii) comprehensive multi-dimensional evaluation demonstrating robustness across diverse data distributions and strong potential for real-world clinical decision support.
📝 Abstract
With the spread of COVID-19 around the globe over the past year, the usage of artificial intelligence (AI) algorithms and image processing methods to analyze the X-ray images of patients' chest with COVID-19 has become essential. The COVID-19 virus recognition in the lung area of a patient is one of the basic and essential needs of clicical centers and hospitals. Most research in this field has been devoted to papers on the basis of deep learning methods utilizing CNNs (Convolutional Neural Network), which mainly deal with the screening of sick and healthy people.In this study, a new structure of a 19-layer CNN has been recommended for accurately recognition of the COVID-19 from the X-ray pictures of chest. The offered CNN is developed to serve as a precise diagnosis system for a three class (viral pneumonia, Normal, COVID) and a four classclassification (Lung opacity, Normal, COVID-19, and pneumonia). A comparison is conducted among the outcomes of the offered procedure and some popular pretrained networks, including Inception, Alexnet, ResNet50, Squeezenet, and VGG19 and based on Specificity, Accuracy, Precision, Sensitivity, Confusion Matrix, and F1-score. The experimental results of the offered CNN method specify its dominance over the existing published procedures. This method can be a useful tool for clinicians in deciding properly about COVID-19.