Abstract
Pneumonia, a lung inflammation and consolidation disorder, poses diagnostic challenges necessitating accurate detection. This paper introduces an innovative automated approach using segmented lung morphology and texture attributes from Chest X-ray (CXR) images. Unlike conventional methods analyzing the entire CXR, our focus narrows to segmented lung regions. Discriminative ranking of extracted features enhances the categorization of CXR images into pneumonia and normal cases. Diverse machine learning classifiers are evaluated, yielding a compelling 86\% accuracy—validating our method's efficacy in distinguishing pneumonia from normal cases. This study offers a robust and efficient diagnostic avenue for improved pneumonia differentiation.
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