CAD System for Lung Cancer Using Statistical Model and Biomarkers

The achievement of image processing techniques and Computer Aided Diagnosis (CAD) systems has demonstrated to be an effectual system for the improvement of radiologists, diagnosis, especially in the case of Medical Image Processing. Screening is justice when there is evidence that it will extend lives at reasonable cost and acceptable levels of risk. In this paper, we present an automatic computer-aided diagnosis (CAD) system for early detection of lung cancer by analyzing chest computed tomography (CT) images and the patient demographic data, blood test result. A detection of the lung cancer in its early stage can be helpful for medical treatment to limit the danger. Most traditional medical diagnosis systems are founded on huge quantity of training data and takes long processing time. So for reducing these problems the Hidden Markov Model is proposed. This method will increase the diagnosis confidence and also reduce the time utility.