%R 10.1109/ICCISci.2012.6297257
%D 2012
%J 2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 - Conference Proceedings
%L scholars2777
%O cited By 16; Conference of 2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 ; Conference Date: 12 June 2012 Through 14 June 2012; Conference Code:93334
%X Cancer is a leading cause of death worldwide. Lung cancer is a type of cancer that is considered as one of the most leading causes of death globally. In Malaysia, it is the 3rd common cancer type and the 2nd type of cancer among men. In this paper, machine learning techniques have been utilized to develop a computer-aided diagnosis system for lung cancer. The system consists of feature extraction phase, feature selection phase and classification phase. For feature extraction/selection, different wavelets functions have been applied in order to find the one that produced the highest accuracy. Clustering-K-nearest-neighbor algorithm has been developed/utilized for classification. Japanese Society of Radiological Technology's standard dataset of lung cancer has been used to test the system. The data set has 154 nodule regions (abnormal) and 92 non-nodule regions (normal). Accuracy levels of over 96 for classification have been achieved which demonstrate the merits of the proposed approach. © 2012 IEEE.
%K Accuracy level; Causes of death; Computer aided diagnosis systems; Computer-aided diagnosis system; Data sets; Feature extraction/selection; Lung Cancer; Machine learning techniques; Malaysia; On-machines, Biological organs; Computer aided diagnosis; Feature extraction; Information science; Learning systems; Radiology; Statistical tests; Technology, Diseases
%P 295-300
%C Kuala Lumpur
%V 1
%A H.R.H. Al-Absi
%A B.B. Samir
%A K.B. Shaban
%A S. Sulaiman
%T Computer aided diagnosis system based on machine learning techniques for lung cancer