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A Genetic Algorithm Approach to Feature Selection for Computer Aided Detection of Lung Nodules

Sprague, Matthew J.
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480465837455442

Abstract Details

2016, Master of Science (M.S.), University of Dayton, Electrical Engineering.
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.
Russell Hardie (Advisor)
Temesguen Messay (Committee Member)
Vijayan Asari (Committee Member)
50 p.
Electrical Engineering
genetic algorithm; feature selection; lung nodule detection; computed tomography; chest radiography

Recommended Citations

Citations

  • Sprague, M. J. (2016). A Genetic Algorithm Approach to Feature Selection for Computer Aided Detection of Lung Nodules [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480465837455442

    APA Style (7th edition)

  • Sprague, Matthew. A Genetic Algorithm Approach to Feature Selection for Computer Aided Detection of Lung Nodules. 2016. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480465837455442.

    MLA Style (8th edition)

  • Sprague, Matthew. "A Genetic Algorithm Approach to Feature Selection for Computer Aided Detection of Lung Nodules." Master's thesis, University of Dayton, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1480465837455442

    Chicago Manual of Style (17th edition)

dayton1480465837455442
531
© 2016, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.