基于smo的直方图识别常见肺部疾病的系统

2013 7th International Symposium on Medical Information and Communication Technology (ISMICT) Pub Date : 2013-03-06 DOI:10.1109/ISMICT.2013.6521711

R. R. Cruz, T. Roque, J. D. Rosas, Charles Vincent M. Vera Cruz, M. Cordel, J. Ilao, A. P. Rabe, J. P. Petronilo

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引用次数: 4

摘要

x光片是医生用来识别肺部异常的可视化辅助工具。虽然数字化的x射线图像是可用的，但医学专家通过模式识别进行诊断是手动完成的。因此，本文提出了一个利用机器学习对正常、胸腔积液和气胸三种肺部情况进行模式识别和分类的系统。采用两种直方图均衡化技术，采用序贯最小优化(SMO)实现了76.19%和78.10%的准确率。

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SMO-based System for identifying common lung conditions using histogram

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).

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2013 7th International Symposium on Medical Information and Communication Technology (ISMICT)

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