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
Book Chapter
Source Title
Mechatronics and Machine Vision in Practice 3
First Page
91
Last Page
108
Publication Date
4-4-2018
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. © Springer International Publishing AG, part of Springer Nature 2018.
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Digitial Object Identifier (DOI)
10.1007/978-3-319-76947-9_7
Recommended Citation
de la Cruz, R. G., Roque, T. C., Rosas, J. G., Vera Cruz, C. M., Cordel, M. O., & Ilao, J. P. (2018). iXray: A machine learning-based digital radiograph pattern recognition system for lung pathology detection. Mechatronics and Machine Vision in Practice 3, 91-108. https://doi.org/10.1007/978-3-319-76947-9_7
Disciplines
Computer Sciences
Keywords
Pattern perception; Diagnostic imaging; Lungs—Imaging
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