SMO-based system for identifying common lung conditions using histogram

• 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., Rabe, Adrian Paul J., Parungao, Petronilo J.
• Format: text
• Published: Animo Repository 2013
• Subjects: Pattern recognition systems; Lungs—Diseases—Imaging; Support vector machines; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3488; https://animorepository.dlsu.edu.ph/context/faculty_research/article/4490/type/native/viewcontent/ISMICT.2013.6521711
• 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 three lung conditions, namely Normal, Pleural Effusion and Pneumothorax cases. Using two histogram equalization techniques, the designed system achieves an accuracy rate of 76.19% and 78.10% by using Sequential Minimal Optimization (SMO). © 2013 IEEE.