Enhanced Detecting System for Computer-Aided Diagnosis of CT Lung Cancer
Umar S. Alqasemi1, Ahmed A. Qashgari2, Mukhtar M. Alansari3
1Umar S. Alqasemi, Department of Biomedical Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia.
2Ahmed A. Qashgari, Department of Biomedical Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia.
3Mukhtar M. Alansari, Department of Biomedical Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia.
Manuscript received on 18 October 2018 | Revised Manuscript received on 27 October 2018 | Manuscript published on 30 October 2018 | PP: 11-14 | Volume-8 Issue-1, October 2018 | Retrieval Number: A5473108118/18©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: According to The American Cancer Society, In the US. they estimated that there will be 160,390 deaths from lung cancer and that 213,380 new cases will be diagnosed in 2007. The accurate diagnosis of the lung nodule in case of determine either malignant or benign requires a lot of resources to read the ever increasing volume of detecting nodules on high resolution computed tomography (HRCT). So, the motivation for developing and enhancing the performance and accuracy of computer-aided-diagnosis systems (CADx) for HRCT has been the focus of many research groups to alleviate this burden. In this work, we report the results of a new CADx system that provides enhanced detecting performance. We utilize an optimized set of texture features using feature selection and apply the new system using Datasets provided by National Cancer Institute (NCI)’s and The Cancer Imaging Archive (TCIA). The results compared well to previous work with classification accuracy performance of 89.4% and 86% sensitivity and 93% specificity. The implementation details and analysis of the proposed system are described in this paper. The results of the new system will contribute a significant impact on the accuracy of such systems and hence the enhancement of detecting process outcome
Keywords: Computer-Aided Diagnosis, Digital Mammogram, Gabor Wavelets Feature, Support Vector Machines, Classification.
Scope of the Article: Classification