Research Article Open Access

A Computer Aided Diagnosis System for Lung Cancer Detection\Using Support Vector Machine

M. Gomathi1 and P. Thangaraj2
  • 1 Department of MCA, Velalar College of Engineering and Technology, Erode 638 012, TamilNadu, India
  • 2 Department of CSE, Bannari Amman Institute of Technology, Erode, TamilNadu, India


Problem statement: Computer Tomography (CT) has been considered as the most sensitive imaging technique for early detection of lung cancer. Approach: On the other hand, there is a requirement for automated methodology to make use of large amount of data obtained CT images. Computer Aided Diagnosis (CAD) can be used efficiently for early detection of Lung Cancer. Results: The usage of existing CAD system for early detection of lung cancer with the help of CT images has been unsatisfactory because of its low sensitivity and False Positive Rates (FPR). This study presents a CAD system which can automatically detect the lung cancer nodules with reduction in false positive rates. In this study, different image processing techniques are applied initially in order to obtain the lung region from the CT scan chest images. Then the segmentation is carried with the help of Fuzzy Possibility C Mean (FPCM) clustering algorithm. Conclusion/Recommendations: Finally for automatic detection of cancer nodules, Support Vector Machine (SVM) is used which helps in better classification of cancer nodules. The experimentation is conducted for the proposed technique by 1000 CT images collected from the reputed hospital.

American Journal of Applied Sciences
Volume 7 No. 12, 2010, 1532-1538


Submitted On: 30 November 2010 Published On: 31 December 2010

How to Cite: Gomathi, M. & Thangaraj, P. (2010). A Computer Aided Diagnosis System for Lung Cancer Detection\Using Support Vector Machine. American Journal of Applied Sciences, 7(12), 1532-1538.

  • 22 Citations



  • Computer Aided Diagnosis (CAD)
  • Support Vector Machine (SVM)
  • False Positive Rates (FPR)
  • Fuzzy Possibilistic C Mean (FPCM)
  • Support Vector Machine (EVM)
  • Computer Tomography (CT)
  • Possibility C-Means (PCM)
  • Fuzzy C-Means (FCM)
  • Artificial Neural Network (ANN)