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SMO-based system for identifying common lung conditions using histogram
Authors
Macario O. Cordel
Ria Rodette G. De La Cruz
+6 more
Joel P. Ilao
Petronilo J. Parungao
Adrian Paul J. Rabe
Trizia Roby Ann C. Roque
John Daryl G. Rosas
Charles Vincent M. Vera Cruz
Publication date
1 January 2013
Publisher
Animo Repository
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
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GreenPrints Institutional repository of De La Salle Medical and Health Sciences Institut
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Last time updated on 03/12/2021
Animo Repository - De La Salle University Research
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Last time updated on 03/12/2021