Pneumonia remains one of the leading causes of mortality worldwide, particularly among children under five and the elderly. Early and accurate diagnosis through chest X-ray interpretation is critical, yet manual analysis by radiologists is time-consuming, subjective, and prone to inter-observer variability. This paper presents a deep learning-based approach for automated pneumonia detection from chest X-ray images using transfer learning with pre-trained convolutional neural network (CNN) architectures. We evaluate the performance of three widely adopted models — ResNet50, VGG16, and DenseNet121 — on the publicly available Kaggle Chest X-Ray Images (Pneumonia) dataset containing 5,856 labeled images. The models are fine-tuned with data augmentation techniques to improve generalization. Our experimental results demonstrate that DenseNet121 achieves the highest classification accuracy of 93.27%, with a recall of 97.44% for pneumonia-positive cases, outperforming both ResNet50 (91.83%) and VGG16 (90.06%). The proposed framework offers a reliable, efficient, and scalable computer-aided diagnostic (CAD) tool that can assist radiologists in clinical decision-making, particularly in resource-constrained healthcare settings.
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