Title

A genetic algorithm approach to feature selection for computer aided detection of lung nodules

Date of Award

2016

Degree Name

M.S. in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Advisor: Russell C. Hardie

Abstract

Lung cancer is responsible for the majority of cancer related deaths in the United States. One way to improve the chance of survival is early detection of Lung Nodules. Lung nodules are small, spherical, potentially cancerous growths within the lung. Several Computer Aided Detection (CAD) systems have been developed to aid in the detection of lung nodules both in computed tomography (CT) and chest radiograph scans. To increase performance and reduce the number of false positives, or misclassifications, in the detection, a feature selection technique is often applied to CAD systems. Feature selection is a method of selecting an optimal subset of features from all features calculated. In this case, a feature is defined as a quantitative characteristic calculated for a potential lung nodule directly from the input scan. Examples of simple features calculated for CAD systems include size, brightness, and shape of potential lung nodules. Common algorithms for feature selection include genetic algorithms and sequential forward selection. This paper proposes a genetic algorithm approach to feature selection for lung nodule CAD systems. Using existing CAD systems with our new feature selection technique, performance is evaluated on both CT scans using the LIDC-IDRI dataset as well as Chest Radiograph scans using the JRST dataset. A total number of 503 features are evaluated for the CT CAD system and 117 features for chest radiographs. Both classification systems utilize the Fisher Linear Discriminant (FLD) classifier. A composite GA fitness function is implemented capable of minimizing the number of false positives in addition to the size of the subset selected. Experimental results indicate that for CAD systems employing a high number of features, a genetic algorithm approach is superior compared to sequential forward selection in both Computed Tomography and Chest Radiography CAD systems.

Keywords

Genetic algorithms, Cancer Imaging, Lungs Tomography, Chest Radiography, Electrical Engineering, genetic algorithm, feature selection, lung nodule detection, computed tomography, chest radiography

Rights Statement

Copyright 2016, author

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