SMO-based System for Identifying Common Lung Conditions Using Histogram

Authors

Ria Rodette G. de la Cruz, Center for Automation Research Laboratory, Computer Technology Department, De La Salle University-Manila
Trizia Roby-Ann C. Roque, Center for Automation Research Laboratory, Computer Technology Department, De La Salle University-Manila
John Daryl G. Rosas, Center for Automation Research Laboratory, Computer Technology Department, De La Salle University-Manila
Charles Vincent M. Vera Cruz, Computer Technology Department, De La Salle University-Manila
Macario O. Cordel II, Computer Technology Department, De La Salle University-Manila
Joel P. Ilao, Computer Technology Department, De La Salle University-Manila
Adrian Paul J. Rabe, Department of Internal Medicine, University of the Philippines
Petronilo J. Parungao, Jr., Department of Radiology, De La Salle Health Sciences Institute

Publication Date

2013

Document Type

Article

Publication Title

2013 7th International Symposium on Medical Information and Communication Technology (ISMICT)

Abstract

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).

First Page

112

Last Page

116

APA Citation

de la Cruz, R. G., Roque, T. C., Rosas, J. G., Vera Cruz, C. M., Cordel II, M. O., Ilao, J. P., Rabe, A. J., & Parungao, Jr., P. J. (2013). SMO-based System for Identifying Common Lung Conditions Using Histogram. 2013 7th International Symposium on Medical Information and Communication Technology (ISMICT), 112-116. 10.1109/ISMICT.2013.6521711