A Deep Learning Approach for Multiclass Pneumonia Detection in Chest X-Ray Images

Abstract

Pneumonia is a significant cause of mortality, particularly in children under five. Accurate detection of pneumonia from Chest X-ray (CXR) images is crucial in mitigating diagnostic errors common in manual radiographic analysis. This study leverages deep learning models to enhance the detection of multiclass pneumonia (normal, bacterial, and viral) using CXR images. We utilized a dataset comprising 5,863 multiclass pneumonia CXR samples. Data augmentation and regularization techniques were applied to address class imbalance and overfitting. Pre-trained models, including EfficientNet, MobileNet, RegNet, and ViT, were fine-tuned using the PyTorch framework, with transfer learning employed to optimize training. Model performance was assessed using accuracy, precision, recall, and specificity. The fine-tuned models achieved high classification accuracy, with EfficientNet and ConvNext models achieving accuracy scores of 83% and 82%, respectively. Data augmentation and regularization significantly improved the models' generalization, reducing overfitting and improving predictive accuracy. The proposed deep learning models provide an efficient and accurate tool for multiclass pneumonia detection from CXRs. These models have the potential to support healthcare professionals in making more accurate diagnoses.