Pneumonia Screening From Radiology Images Using Homomorphic Transformation Filter‐Based <scp>FAWT</scp> and Customized <scp>VGG</scp> ‐16

ABSTRACT Pneumonia, attributable to pathogens and autoimmune disorders, accounts for approximately 450 million cases annually. Chest x‐ray analysis remains the gold standard for pneumonia detection, and DL has revolutionized the study of high‐dimensional data, including images, audio, and video. This research enhances and validates a CAD system for distinguishing pneumonia from normal health states using x‐ray imaging. This paper presents a novel methodology that integrates CLHAE and Homographic Transformation Filter‐based Flexible Analytical Wavelet Transform (HTF‐FAWT) for image decomposition, enabling systematic decomposition of pre‐processed input images into four distinct sub‐band images across six hierarchical levels. Feature extraction employs the VGG‐16 Deep Learning techniques, with the extracted features subsequently classified by a support vector machine that integrates Morlet, Mexican‐hat wavelet, and radial basis function kernels. Employing tenfold cross‐validation, our model exhibited remarkable classification performance, achieving an accuracy of 97.51%, specificity of 97.77%, and sensitivity of 96.5% in spotting pneumonia via Chest x‐rays. The utility of feature maps and Grad‐CAM analysis in highlighting critical regions for accurate prediction was confirmed, offering visual validation of the model's efficacy. Statistical examinations validate the superior performance of our proposed framework, demonstrating its potential as an expedient diagnostic tool for medical imaging specialists in rapidly detecting pneumonia. It demonstrates the effectiveness of various classifiers for classification, with the proposed method outperforming state‐of‐the‐art approaches. The proposed CAD system enhances pneumonia diagnosis with high accuracy (97.51%), Grad‐CAM visualization, and automated interpretation, enabling faster, reliable screening and clinical integration and reducing reliance on manual assessment in radiology.