iXray: A machine learning-based digital radiograph pattern recognition system for lung pathology detection
College
College of Computer Studies
Department/Unit
Computer Technology
Document Type
Conference Proceeding
Source Title
21st Mechatronics and Machine Vision in Practice, M2VIP 2015
Publication Date
1-1-2015
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 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.
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Recommended Citation
De La Cruz, R. G., Roque, T. C., Rosas, J. G., Vera Cruz, C. M., Cordel, M. O., & Ilao, J. P. (2015). iXray: A machine learning-based digital radiograph pattern recognition system for lung pathology detection. 21st Mechatronics and Machine Vision in Practice, M2VIP 2015 Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/344
Disciplines
Computer Sciences
Keywords
Pattern perception; Diagnostic imaging; Lungs—Imaging
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