SMO-based system for identifying common lung conditions using histogram
College
College of Computer Studies
Department/Unit
Computer Technology
Document Type
Conference Proceeding
Source Title
International Symposium on Medical Information and Communication Technology, ISMICT
First Page
112
Last Page
116
Publication Date
8-15-2013
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). © 2013 IEEE.
html
Digitial Object Identifier (DOI)
10.1109/ISMICT.2013.6521711
Recommended Citation
De La Cruz, R. G., Roque, T. C., Rosas, J. G., Vera Cruz, C. M., Cordel, M. O., Ilao, J. P., Rabe, A. J., & Parungao, P. J. (2013). SMO-based system for identifying common lung conditions using histogram. International Symposium on Medical Information and Communication Technology, ISMICT, 112-116. https://doi.org/10.1109/ISMICT.2013.6521711
Disciplines
Computer Sciences
Keywords
Pattern recognition systems; Lungs—Diseases—Imaging; Support vector machines
Upload File
wf_yes
