A comprehensive review of Lung nodule identification using an effective Computer-Aided Diagnosis (CAD) System

Lung cancer is a nothing but abnormal swelling of lung tissues and could be a life threatening one. As per statistics, it is responsible for more deaths than any other type of cancer. It is important to identify and treat this anomaly from the patient's perspective. For identifying the tumour cells from CT scans, numerous image-processing and soft-computing procedures are used. CT scan images are mostly used in the image processing since these are high quality and clear images with more ppi (pixel per inch). Using this method, small nodes of tissues (nodules) can be found. In primary finding of lung cancer, patient's probabilities of existence are increased. Hence, an effective CAD system for lung tumour detection has been projected. This system comprises three points: initial level processing, segmentation and classification of nodules. The study of lung disorders requires accurate segmentation of lung images which is very important in detecting lung cancer. Lung images contains noise and weak boundaries so, accurate detection of lung nodule is very difficult or challenging task. This paper covers the comprehensive review of methods used for lung nodule detection.