부산대학교 도서관

Lung cancer is life-threatening diseases and it now affects all people regardless of gender. Precise lung tumor classification helps to diagnosis lung cancer early, which decreases the rate of death of lung cancer, but it is hard to detect early. Harmless or malignant is known as a lung tumor. It is innocuous when the tumor cells were healthy, when the cells are abnormal and can grow hysterically, they are cancerous cells, and the tumor is in curable. A classifier based on Deep Convolutional Neural Networks (DCNN), which classifies the lung tumor composed of different fully connected pooling and Convolutional layers. Three architectures were defined for DCNN classifier each one is trained with different patch size. DCNN is applied to the CT image for classification of benign and malignant lung tumor. The proposed architectures were examined on the LIDC database and cross checked with other classifiers result such as Artificial Neural Network Simulation result presents DCNN classifier achieves better performance.