A neural network-based diagnostic method for solitary pulmonary nodules

Several computer-aided diagnostic (CAD) methods for solitary pulmonary nodules (SPNs) have been proposed, which can be divided into two major categories: (1) the morphometric CT method, depending on high-resolution morphometric characteristics from single CT scan and (2) the perfusion CT method, depending on properties of the post-contrast enhancement dynamics obtained from repeated CT scans at predefined time points. The goal of this work is to introduce a neural network-based CAD method of lung nodule diagnosis by combining morphometry and perfusion characteristics by perfusion CT. Compared with previous methods, the proposed approach has the following distinctive features. Firstly, this work develops a very efficient semi-automatic procedure to segment entire nodules. Secondly, reliable nodule classification can be achieved by using only two time-point perfusion CT feature measures (precontrast and 90 s). This greatly reduces the amount of radiation exposure to patients and the data processing time. The effectiveness of the proposed approach is compared with those of several previously developed CAD methods.