Optimizing Chest X-ray Analysis for Pneumonia Detection Through Comparative Evaluation of Transfer Learning CNN Architectures

Pneumonia continues to inflict a major global health burden. This research investigates the application of deep learning to enhance chest X-ray-based pneumonia diagnosis, offering a potentially transformative approach. Five pre-trained Convolutional Neural Networks (CNNs) – MobileNetV2, VGG16, DenseNet121, EfficientNetB0, and ResNet50 – were evaluated for their effectiveness. Each model underwent rigorous preprocessing, including quality filtering, standardization, and data augmentation, to ensure robust and generalizable performance. VGG16 had the highest accuracy, reaching 90.87%, whereas DenseNet121 achieved a well-balanced F1 score of 74.52%. Using the comparative analysis as a basis, a novel hybrid model was created by combining the strengths of VGG16 and DenseNet121 through feature concatenation. This innovative approach yielded exceptional results, surpassing all individual models: an impressive 92.53% accuracy, 83.41% recall, and 81.76% precision. These findings strongly suggest the potential of deep learning to revolutionize pneumonia detection, with the hybrid model promising faster diagnosis, improved clinical decision-making, and ultimately, better patient outcomes. Widespread deployment of this AI-powered system, particularly in resource-constrained regions, could significantly reduce the global burden of pneumonia by providing vital access to timely and accurate diagnosis, ultimately saving lives and reducing healthcare costs.