Lung cancer diagnosis using CT-scan images based on cellular learning automata

Lung cancer has killed many people in recent years. Early diagnosis of lung cancer can help doctors to treat patients and keep them alive. The most common way to detect lung cancer is using the Computed Tomography (CT) image. The systems that are created by the integration of computers and medical science are called Computer Aided Diagnosis (CAD). A CAD system that is adopted for the diagnosis lung cancer, uses lung CT images as input and based on an algorithm helps doctors to perform an image analysis. With the help of CAD, doctors can make the final decision. This paper is a study concerning automatic detection of lung cancer by using cellular learning automata. Images include some unwanted data and some feature that are important for processing; pre-processing improves images by removing distortion and enhance the important features. This system used lung CT scan so we applied some pre-processing method such as Gabor filter and region growing to improve CT images. After pre-processing step according features the lung cancer nodule was extracted. The obtained image through previous steps was entered to cellular learning automata lattice for training and making them possess the ability to detect lung cancer. The obtained results show, the proposed approach can reduce the error rate.