Hybrid deep learning and handcrafted feature fusion for pneumonia detection in chest X-rays

Abstract Pneumonia is a major cause of death worldwide, with nearly 2.2 million deaths every year, including over 700,000 children aged five years and below. Chest X-ray (CXR) imaging is the standard method taken up to diagnose pneumonia; however, such image inspection is shown to be difficult even for expert radiologists. The intricacy in the visual patterns generated in X-ray images often results in misdiagnosis, indicating the importance of efficient and accurate automated substitutes. In this paper, we present a new machine learning-based system that incorporates deep learning combined with handcrafted feature extraction techniques for sophisticated pneumonia detection. We use ResNet-50 for deep feature extraction, with the integration of 2D-discrete wavelet transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) for texture feature extraction, for the intention of gaining helpful spatial and frequency-domain features. The features obtained are inputted into a Support Vector Machine (SVM) classifier, which is optimized for high accuracy and robust prediction. The experimental findings indicate that the proposed model produces a classification accuracy of 97%, accompanied by an F1-score of 0.97, over traditional methods. Through the synergistic integration of handcrafted and deep learning-based feature extraction techniques, our approach presents a trustworthy and efficient solution for automated pneumonia detection. The proposed method has the potential to aid radiologists in providing timely and accurate diagnoses, thus enhancing patient outcomes and curtailing the global burden of pneumonia-related mortality.