Description

Lung cancer is the most important cause of cancer death for both men and women. The early detection of cancer can be helpful in curing the disease completely. So the requirement of techniques to detect the occurrence of cancer nodule in early stage is increasing. There are different technique exists but none of those provide better accuracy of detection. This paper provides a Computer Aided Diagnosis System (CAD) for early detection of lung cancer nodules from the Chest Computer Tomography (CT) images.There are five phases involved in the proposed CAD system. They are extraction of lung region from chest computer tomography images, segmentation of lung region, feature extraction from the segmented region, formation of diagnosis rules form the extracted features and classification of occurrence and non occurrence of cancer in the lung. For segmentation, Modified Fuzzy Possibilistic C-Mean algorithm is used in this paper because of it better segmentation features. For classification, Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are used. The experimentation is performed with the help of real time chest computer tomography images. The experimental result shows that the accuracy of using ELM is better when compared to the usage of SVM for classification.