Classification of Lung Data by Sampling and Support Vector Machine

Abstract

Developing a Computer-Assisted Detection (CAD) system for automatic detection of pulmonary nodules in thoracic CT is a highly challenging research area in the medical domain. It requires the application of state-of-the-art image processing and pattern recognition technologies. The object recognition and feature extraction phase of such a system generates a large number of data set. As there is normally a large quantity of non-nodule objects within this data set while the nodule objects are sparse, a Gaussian mixture model-based sampling method is used to reduce the non-nodule data and thus the classification complexity. The support vector machine, a classifier motivated from the statistical learning theory, is used in the pattern recognition stage of automatic pulmonary nodule detection. After the training process, only support vectors will be used in the classification process. As the support vector machine classifier gives the unique optimal solution, the experiment on the lung nodule data shows a fast and satisfactory classification rate.

Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features

“…The svm type was set to C-SVC (classification) and the default termination criteria were used. Dehmeshki et al [23] used support vector machines effectively on CT-scan image data of the lungs in a Computer-Assisted Detection (CAD) system for automated pulmonary nodule detection in thoracic CT-scan images. We used the support vector machine libSVM by Chang and Lin [24].…”

Identification of QRS complexes in 12-lead electrocardiogram

“…Brown et al (1999) described a successful use of SVMs applied to gene expression data for the task of classifying unseen genes. Dehmeshki, Chen, Casique, and Karakoy (2004) used SVM for the classification of lung data. Chu, Jin, and Wang (2005) applied SVMs for cancer diagnosis based on micro-array gene expression data and protein secondary structure prediction.…”

Measure oriented cost-sensitive SVM for 3D nodule detection

“…However, in the potential nodule classification, the problem has attracted less attention. Only few works consider this problem using simple solutions [3,[6][7][8]. There is still room for improvement on the FPR.…”