@inproceedings{2f107a78cf2c4da7835d961e7d63b4f7,
title = "A statistical feature selection method for lung cancer classification in CT scans",
abstract = "This paper presents a computer aided diagnosis for lung nodules in CT images. The system consists of feature extraction, feature selection and classification. A two-step feature selection process is introduced to reduce the number of coefficients produced in the feature extraction step. This helps in enhancing the classification performance as it removes unneeded and redundant information. The classification rate of the system reached 98.10 % with minimum false negatives and zero false positives.",
keywords = "Lung nodules, cluster k-Nearest Neighbor, computer aided diagnosis, feature selection",
author = "Al-Absi, {Hamada R.H.} and Samir, {Brahim Belhaouari}",
year = "2013",
doi = "10.1063/1.4826054",
language = "English",
isbn = "9780735411845",
series = "AIP Conference Proceedings",
pages = "2524--2527",
booktitle = "11th International Conference of Numerical Analysis and Applied Mathematics 2013, ICNAAM 2013",
note = "11th International Conference of Numerical Analysis and Applied Mathematics 2013, ICNAAM 2013 ; Conference date: 21-09-2013 Through 27-09-2013",
}