Deep Learning in Medical Imaging: CNN Performance in Pneumonia Detection from X-rays

Pneumonia is a serious infection to the respiratory system; it is also a significant health risk, more so in vulnerable populations. Early and accurate detection is paramount for effective treatment and management. In this study, we explore an application of Convolutional Neural Networks for automatized pneumonia detection from chest X-ray images. Our model is trained and evaluated using a dataset that includes 624 images, 390 images labeled as Pneumonia and 234 labeled as Normal. According to this, performance metrics indicate performance in robust capabilities of classification, with overall accuracy at 93%. The model exactly had a precision of 0.93, a recall of 0.96, and an F1-score of 0.94 in pneumonia detection and a precision of 0.92, a recall of 0.88, and an F1-score of 0.90 in the normal cases. The macro averages are 0.93 for precision, 0.92 for recall, and 0.92 for the F1 score; the weighted averages reproduced the same result. These findings prove that the CNN can be a helpful tool within a clinical set-up for timely diagnosis of pneumonia, hence doing timely medical interventions for better patient outcome.