Classification of Lung Tumor from CT Images using Computer Aided Diagnosis Scheme

Lung cancer is the second most prevalent type of cancer. Early detection and classification of lung tumors helps in better survival. The paper presents a computer aided diagnosis (CAD) system for chest computed tomography (CT) images that focus on a new algorithm to classify lung tumor into benign or malignant using advanced neural network based classifier. Statistical textural features are extracted which aids in better classification. The accuracy of the algorithm is also computed. Methods: Input image is pre-processed by median filtering and segmented to extract the portion of lungs. This is followed by identifying the tumor objects alone from the region of interest of the lungs. Statistical textural features are extracted only from the tumor objects. Classification of tumor into benign and malignant tumor is done using feed forward back propagation neural network classifier. The result shows proposed algorithm gives classification accuracy above 99% which suggests that the developed CAD system has greater potential for automatic classification of lung tumor.