A hybrid deep learning model for pneumonia detection in chest x-ray images

Abstract

Pneumonia is the leading respiratory cause of illness worldwide and has a significant impact on global health, challenging health systems, especially in resource-constrained scenarios, for immediate diagnosis. Timely and accurate diagnosis is essential to improve management and reduce mortality. Chest X-ray is a frequently used diagnostic tool, valued for its availability, rapidity, cost-effectiveness, and accuracy in detecting respiratory conditions. Recent developments in deep learning (DL) and machine learning (ML) have transformed the field of medical imaging, leading to improved diagnostics. In this context, a novel deep learning approach for automatic pneumonia detection in chest X-ray images is presented. Based on the advanced CNN architecture InceptionV3, this model is more effective at capturing features from data frames and employs LSTM networks to learn sequential data efficiently. This integration enables the model to learn both the spatial information of medical images and the temporal relationships, which enhances classification accuracy. Extensive experiments on public datasets with existing CNN-based models demonstrate that the proposed hybrid architecture surpasses traditional CNN models in accuracy, F1-score, and ROC-AUC-score, achieving 91.67%, 93.47%, and 97%, respectively. These results further support the potential of hybrid deep learning approaches as innovative methodologies for improving diagnostic accuracy and assisting healthcare professionals in lung infection diagnosis.