AN IMPERATIVE STUDY OF EFFICIENT IMAGE PROCESSING ALGORITHMS FOR LUNG CANCER DETECTION, SEGMENTATION AND CLASSIFICATION: A REVIEW

The Lung cancer is one of the leading health issues contributing to the high mortality ratein the universe. The survival rate of lung cancer is very low compared to other cancers. At early stage only 15% of the patients with lung cancers were caught over decades and when it spreads to other organs the survival rate drops to 3.5%. Early detection of the disease is needed to reduce the mortality rate and help to increase the chances of survival rate. Over the last few years, the survival rate has increased from 15% to 49% due to the diagnosis of the disease at an initial stage. A computed Tomography (CT) image is effective to identify the presence of lung cancer. CT images with various image processing algorithms helps in early diagnosis of this disease. A good amount of research work has been carried out towards CAD system for the detection of lung cancer using CT images. It is divided into four stages: preprocessing nodule detection, nodule segmentation and classification. This paper presents in detail the imperative survey on various techniques that have been used in Pre-processing, nodule segmentation, detection and classificatio