Deep Learning-Based Chest X-Ray Classification For Pneumonia Detection Using Transfer Learning

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.