A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks

One of the most conspicuous developments in the unprecedented worldwide epidemic of COVID-19 is the pressing demand for reliable diagnostic tools. Utilizing artificial intelligence (AI) and image processing algorithms, this work proposes a novel 19-layer Convolutional Neural Network (CNN) for accurate COVID-19 detection from chest X-ray images. This CNN architecture supports structure with single/multiple labels for three classes (i.e., for classification between layers like viral pneumonia, normal, and COVID-19) and four classes (i.e., lung opacity, normal, COVID-19, and pneumonia). Across the accuracy, specificity, precision, sensitivity, confusion matrix, F1-score, and other metrics, our model was compared to periutils net from the literature such as popular pre-trained networks(Inception, AlexNet, ResNet50, SqueezeNet, VGG19). Experimental results show that the proposed CNN outperforms existing methods, providing an effective diagnostic tool with the potential for clinical usage. This means AI algorithms as advanced as Cogito could start making decisions about COVID-19 and inform clinicians about how to handle the case.