A comparative study of machine learning methods for lung diseases diagnosis by computerized digital imaging'"

Abstract

In this study. we tested and comparcd several state-of-art machine-learning

niethods for automated classification of obstructive lung diseases based on the

features from text,ure analysis using HRCT (High 1Zesolution Computerized

Tomography) images. I-IRCT can provide accurate images for the detection of

various obstructive lung diseases, including centrilobular emphysema, panlobuclar

emphysema and constrictive bronchiolitis. Features on the HHCT images.

however, can be subtle, particularly in the early stages of disease. and image

based diagnosis is subject to jnter-observer variation. In order to support the

clinical diagnosis and improve its accuracy. three different types of automated

classification systems were developed and comparcd based on the classification

performance and clinical applicability. Not only Bayesian classifier, a typical kind

of statistic method, but also ANX (Artificial Neural Network) and SVM (Support Vector Machine) were employed. We tested these three classifiers for the

differentiation of normal and three types of obstructive lung diseases. The ANN

showed the best performance of 86.0% overall sensitivity and there is significant

difference among other classifiers (one-way ANOVA, ~ ( 0 . 0 1 )I.n discussion, we

addressed what characteristic of each classifier made differences in the

performance and which classifier was more suitable for clinical applications and

proposed appropriate way to choose the best classifier and determine its optimal

parameters to discriminate the diseases better. This result can be applied to the

classifier for differentiation of other diseases.