Advanced pneumonia classification using transfer learning on chest X-ray data with EfficientNet and ResNet

Abstract

Pneumonia is a serious lung infection that demands accurate and timely diagnosis to reduce mortality. This study explores the use of deep learning and transfer learning for classifying chest X-ray images into two categories: normal and pneumonia. A total of 5,632 labeled images were used to train and evaluate six pre-trained convolutional neural network (CNN) architectures: EfficientNetB1, B3, B5, B7, ResNet50, and ResNet101. The models were tested across three training scenarios by varying learning rates (LR), batch sizes, and epochs. Among all models, EfficientNetB3 achieved the highest performance, with accuracy of 99.04%, precision of 99.76%, recall of 99.23%, and F1-score of 99.34%. These results indicate that EfficientNetB3 offers a robust and efficient solution for pneumonia detection. This research contributes to the development of intelligent diagnostic tools in the medical field and provides practical guidance for selecting effective deep learning models in clinical imaging applications.