Automated Detection of Early Pulmonary Nodule in Computed Tomography Images

Abstract

Classification of lung cancer in CT scans majorly have two steps, detect all suspicious lesions also known as pulmonary nodules and calculate the malignancy. Currently, a lot of studies are about nodules detection, but some are about the evaluation of nodule malignancy. Since the presence of nodule does not unquestionably define the presence lung cancer and the morphology of nodule has a complex association with malignant growth, the diagnosis of lung cancer requests cautious examinations on each suspicious nodule and integrateed information every nodule. We propose a 3D CNN CAD system to solve this problem. The system consists of two modules a 3D CNN for nodule detec-tion, which outputs all suspicious nodules for a subject and second module train on XGBoost classifier with selective data to acquire the probability of lung malignancy for the subject.