A Two-Phase Pneumonia Detection and Subclassification Model Using Hybridized EfficientNet and Random Forest

This study explores the application of EfficientNet combined with Random Forest for classifying chest X-ray images to detect and categorize pneumonia. Initially, the model was employed to classify X-ray images as either normal or pneumonia-affected, achieving an accuracy of 98.4%. Subsequently, pneumonia cases were further categorized into three distinct types: COVID-19-induced, bacterial, and viral pneumonia, yielding a classification accuracy of 83%. The feature extraction capability of EfficientNet, a state-of-the-art convolutional neural network pre-trained on ImageNet, was leveraged to distill meaningful patterns from the medical images, while Random Forest (known for handling complex, non-linear relationships), served as the classifier for final decision-making process. Notably, the model effectively differentiated between bacterial and viral pneumonia, achieving an accuracy of 86.91%, despite the overlapping features between these two types. This dual-stage approach (first identifying the presence of pneumonia, followed by specific subtype classification) demonstrates the potential for AI-driven diagnostic tools to assist in more accurate and detailed pneumonia diagnosis.