An Early Prediction of Lung Cancer using CT Scan Images

Lung cancer is a common occurrence type in population and one amonglethal cancers. Recently, out of several research presented by diverse health agencies; it obvious that the fatality ratio rising due todelayeddiagnosis lung cancer. Hence, an artificial intelligence-based diagnosis required to find onset nodule micro-calcification, which may support doctors radiologists accurately predict through image processing methods. In this paper, novel technique proposed identify micro-calcification pattern using its physical features. The features considered are reflection coefficients mass densities binned CT lung. measurements reiteratesonce again existence malignant nodule. Then, applying methods thresholding interpolation features, three-dimensional (3D) projected region interest (ROI) achieved respect dimensions. Thus, size calculated from 3D projection. This concept used verify how best classification with 100 images (the presence) 10 normal absence). Apart measurement, method supports SVM classifier act for excellentclassification malignantinput imagesby just two exhibited accuracy 98%.