iXray: A machine learning-based digital radiograph pattern recognition system for lung pathology detection

• Main Authors: De La Cruz, Ria Rodette G., Roque, Trizia Roby Ann C., Rosas, John Daryl G., Vera Cruz, Charles Vincent M., Cordel, Macario O., Ilao, Joel P.
• Format: text
• Published: Animo Repository 2015
• Subjects: Pattern perception; Diagnostic imaging; Lungs—Imaging; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/344
• Institution: De La Salle University

A radiograph is a visualization aid that physicians use in identifying lung abnormalities. Although digitized x-ray images are available, diagnosis by a medical expert through pattern recognition is done manually. Thus, this paper presents a system that utilizes machine learning for pattern recognition and classification of six lung conditions classified into two categories, namely Histogram-based (Normal, Pleural Effusion, and Pneumothorax) and Statistics-based (Cardiomegaly, Hyperaeration, and possible Lung Nodules). Using preprocessing and feature extraction techniques, the designed system achieves an accuracy rate of 92.59% for the Histogram-based lung conditions using Sequential Minimal Optimization (SMO) and 67.22% for the Statistics-based lung conditions using logic operations. © 2015, Mechatronics and Machine Vision in Practice. All rights reserved.