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AuthorAl-Absi, Hamada R. H.
AuthorSamir, Brahim Belhaouari
AuthorShaban, Khaled Bashir
AuthorSulaiman, Suziah
Available date2022-12-21T10:01:46Z
Publication Date2012
Publication Name2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 - Conference Proceedings
ResourceScopus
URIhttp://dx.doi.org/10.1109/ICCISci.2012.6297257
URIhttp://hdl.handle.net/10576/37496
AbstractCancer 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.
Languageen
SubjectAccuracy 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
TitleComputer aided diagnosis system based on machine learning techniques for lung cancer
TypeConference Paper
Pagination295-300
Volume Number1
dc.accessType Abstract Only


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