Hybrid Deep Learning and Machine Learning Framework for Automated Pneumonia Detection from Chest X-ray Images

Abstract

Pneumonia remains one of the leading causes of respiratory illness worldwide and requires timely diagnosis to reduce disease severity and mortality. Interpretation of chest X-ray images is a routine diagnostic procedure; however, manual examination can be time-consuming and is influenced by the experience of radiologists. Computer-aided diagnostic systems based on artificial intelligence have therefore attracted considerable attention as supportive tools for clinical decision-making. This research presents a hybrid framework that combines deep feature extraction with traditional machine learning techniques for automated pneumonia detection from chest X-ray images. Initially, chest X-ray images are preprocessed and analysed using an EfficientNetV2 convolutional neural network to learn representative image features. Instead of directly performing end-toend classification, the extracted deep features are used to train three supervised machine learning classifiers: Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). This hybrid strategy combines the feature representation capability of deep learning with the classification efficiency of conventional machine learning algorithms. To improve model transparency, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to highlight image regions that contribute most to the prediction. Furthermore, the selected classifier is integrated into a Streamlit-based web application that enables users to upload chest X-ray images and obtain real-time diagnostic predictions. Experimental evaluation demonstrates that the Support Vector Machine classifier achieved the highest performance among the evaluated models, with an accuracy of 89.74%, precision of 86.22%, recall of 99.49%, F1-score of 92.38%, and ROC-AUC of 98.21%, indicating strong capability for distinguishing pneumonia from normal chest radiographs. The proposed framework illustrates that combining deep feature extraction with machine learning classifiers can provide accurate, interpretable, and computationally efficient pneumonia detection while supporting practical clinical deployment.