Title :
SMO-based System for identifying common lung conditions using histogram
Author :
de la Cruz, R.R.G. ; Roque, Trizia Roby-Ann C. ; Rosas, J.D.G. ; Vera Cruz, Charles Vincent M. ; Cordel, M.O. ; Ilao, J.P. ; Rabe, A.P.J. ; Petronilo, J.P.
Author_Institution :
Comput. Technol. Dept., De La Salle Univ. - Manila, Manila, Philippines
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).
Keywords :
diagnostic radiography; learning (artificial intelligence); lung; medical image processing; optimisation; pattern classification; support vector machines; SMO-based system; digitized X-ray images; histogram equalization techniques; lung abnormality identification; lung condition identification; machine learning; medical expert; normal lung conditions; pattern classification; pattern recognition; physicians; pleural effusion case; pneumothorax case; radiograph; sequential minimal optimization; Adaptive equalizers; Biomedical imaging; Histograms; Kernel; Lungs; Support vector machines; Training; Image Histogram; Pattern Recognition; Support Vector Machines;
Conference_Titel :
Medical Information and Communication Technology (ISMICT), 2013 7th International Symposium on
Conference_Location :
Tokyo
Print_ISBN :
978-1-4673-5770-8
Electronic_ISBN :
2326-828X
DOI :
10.1109/ISMICT.2013.6521711
