Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifie

Recognition and prediction of lung cancer in the earliest reference point stage can be very useful to improve the survival rate of patients. But diagnosis of cancer is one the major challenging task for radiologist. For detecting, predicting and diagnosing lung cancer, an intelligent computer-aided diagnosis system can be very much useful for radiologist. This paper proposed an efficient lung cancer detection and prediction algorithm using multi-class SVM (Support Vector Machine) classifier. Multi-stage classification was used for the detection of cancer. This system can also predict the probability of lung cancer. In every stage of classification image enhancement and segmentation have been done separately. Image scaling, color space transformation and contrast enhancement have been used for image enhancement. Threshold and marker-controlled watershed based segmentation has been used for segmentation. For classification purpose, SVM binary classifier was used. Our proposed technique shows higher degree of accuracy in lung cancer detection and prediction