Lung-Centric Feature Analysis for Accurate Pneumonia Detection in Chest X-RAY Images


Najah Alsubaie1, Tahani Alqahtani1 and Syarifah Bahiyah Rahayu2, 1Princess Nourah bint Abdulrahman University (PNU), Saudi Arabia, 2National Defense University of Malaysia, Malaysia


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.


lung, chest X-ray, machine learning, pneumonia, morphology, texture

Full Text  Volume 14, Number 1