Detection of pneumonia from chest X-rays using convolutional neural networks

Abstract

Pneumonia detection from chest X-rays is crucial for early diagnosis, and deep learning models –specifically convolutional neural networks (CNNs) – have shown promise in automating this process. In this study, a CNN using the DenseNet-121 architecture was developed and trained, referred to as LDCS2, to classify chest X-ray images as pneumonia or normal, using a combined dataset from three publicly available sources. The CNN approach was chosen over Vision Transformers (ViT) due to lower computational requirements and better performance with limited data. A traditional training, validation, and testing split was used instead of k-fold cross-validation to reduce execution time. LDCS2 demonstrated excellent discrimination between pneumonia and normal images alongside high computational efficiency. These findings highlight the potential of DenseNet-based CNNs for automated pneumonia diagnosis, particularly in resource-constrained settings.