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.
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Notes
- 1.
Philippine Health Statistics. “Leading causes of morbidity”. Philippine Department of Health. Retrieved June 3, 2017 from http://www.doh.gov.ph/node/1482.
- 2.
Global Health Observatory Data Repository. “Global Burden Disease 2015”. World Health Organization. Retrieved June 3, 2017 from http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html.
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Acknowledgements
We would like to give gratitude to Dr. Lourd Loreto, Dr. Adrian Rabe of the Philippine General Hospital and Dr. Jun Parungao of De La Salle Health Sciences Institute, for imparting with us the basic medical knowledge needed.
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de la Cruz, R.R.G., Roque, T.RA.C., Rosas, J.D.G., Vera Cruz, C.V.M., Cordel, M.O., Ilao, J.P. (2018). iXray: A Machine Learning-Based Digital Radiograph Pattern Recognition System for Lung Pathology Detection. In: Billingsley, J., Brett, P. (eds) Mechatronics and Machine Vision in Practice 3. Springer, Cham. https://doi.org/10.1007/978-3-319-76947-9_7
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