Lung cancer is a disease of uncontrolled cell growth in tissues of the lung. Lung cancer is the most critical reason for death, but there is a big chance for the patient to be cured if he or she is correctly diagnosed in early stage of his or her case. Medical chest images are considered the most widely used and reliable method for the detection of lung cancer, the serious mistake in some diagnosing cases giving bad results and causing the death.
The Computer Aided Diagnosis systems are necessary to support the medical staff to achieve high capability and effectiveness. Clinicians could improve treatment methods by using image mining techniques. For detecting lung nodules, number of tests should be required from the patient. Automated diagnosis system for prediction of lung cancer by using image mining techniques plays an important role in time and performance which decreases mortality rate because of early detection of lung cancer.
In this research, we will present an overview of some existing image mining techniques for diagnosis of lung cancer at initial stage by analyzing the medical chest images, which assist radiologists for their chest images interpretation of lung cancer. This research analyzes various image processing and classification techniques and their efficiency used for predicting lung cancer.