Published Paper Details:

Lung cancer has been responsible for the major deaths all over the world for the past few decades. The mortality rate of lung cancer has been at the peak in comparison with all other cancers worldwide. The most adequate way followed by radiologists for analysis of cancer in lungs is using computed tomography as it provides a detailed picture of cancer cells. The usual method followed is visual interpretation which can sometimes be wrong due to human error and is always time consuming. Therefore an efficient Lung cancer detection system is required by everyone all over the world. Thus this paper presents an automated approach for premature analysis of lung cancer using CT scan images. A numeral methodologies have been discovered to increase the efficiencies of the system for detecting the tumours in the lungs.Thus this algorithm is proposed using support vector machine, watershed segmentation and extraction of geometrical and textural features using gray level co-occurance matrix.