Detection of Lung Nodule using Multiscale Wavelets and Support Vector Machine
1K.P.Aarthy, Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Erode (Dt), Tamilnadu, India.
2U.S.Ragupathy, Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Erode (Dt), Tamilnadu, India.
Manuscript received on July 01, 2012. | Revised Manuscript received on July 04, 2012. | Manuscript published on July 05, 2012. | PP: 32-36 | Volume-2, Issue-3, July 2012. | Retrieval Number: C0667052312 /2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: 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: Lung cancer is the most common and leading cause of death in both men and women. Lung nodule, an abnormality which leads to lung cancer is detected by various medical imaging techniques like X-ray, Computerized Tomography (CT), etc. Detection of lung nodules is a challenging task, since the nodules are commonly attached to the blood vessels. Many studies have shown that early diagnosis is the most efficient way to cure this disease. This paper aims to develop an efficient lung nodule detection scheme by performing nodule segmentation through multiscale wavelet based edge detection and morphological operations; classification by using a machine learning technique called Support Vector Machine (SVM). This methodology uses three different types of kernels like linear, Radial Basis Function (RBF) and polynomial, among which the RBF kernel gives better class performance with a sensitivity of 92.86% and error rate of 0.0714.
Keywords: Lung Nodule, Multiscale Wavelets, Support Vector Machine, Wavelet Transform.