Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.
Journal:
Biomedical engineering online
Published Date:
Feb 12, 2015
Abstract
BACKGROUND: Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool.
Authors
Keywords
Algorithms
Area Under Curve
Automation
Humans
Imaging, Three-Dimensional
Incidental Findings
Lung Neoplasms
Pattern Recognition, Automated
Radiographic Image Interpretation, Computer-Assisted
Referral and Consultation
ROC Curve
Sensitivity and Specificity
Solitary Pulmonary Nodule
Support Vector Machine
Tomography, X-Ray Computed
Wavelet Analysis