An Effective and Efficient Feature Selection Method for Lung Cancer Detection

Medical image data is growing rapidly. Lung cancer considers to be the most common cause of death among people throughout the world. Early lung cancer detection can increase the chance of people survival. The 5 year survival rate for lung cancer patient increases from 14 to 49% if the disease is detected in time. Computed Tomography can be more efficient than X ray for detecting lung cancer in time. But the problem seemed to merge due to time constraint in detecting the presence of lung cancer. MATLAB have been applied for the study of these techniques. Feature selection is a method to reduce the number of features in medical applications where the image has hundreds or thousands of features. In order to extract the accurate features of an image, an image need to be processed for its effective retreival. Image feature selection is an essential task for recognizing the image and it can be done for overcoming classification problems. However, the quality of the image recognition tasks can be improved with the help of better classification accuracy for enhancing the retrieval performance.