Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system.
In this paper, a novel extension of neural network-based fuzzy model has been proposed to detect lung nodules. The proposed model can automatically identify a set of appropriate fuzzy inference rules, and refine the membership functions through the steepest gradient descent-learning algorithm. Twenty-nine clinical cases involving 583 thick section CT images were tested in this study. Receiver operating characteristic (ROC) analysis was used to evaluate the proposed autonomous pulmonary nodules detection system and yielded an area under the ROC curve of Azs=0.963. The overall detection sensitivity of the proposed method was 89.3% (with p-value less than 0.001), and the false positive was as low as 0.2 per image. This result demonstrates that the proposed neural network-based fuzzy system resolves the most suitable fuzzy rules, improves the detection rate, and reduces false positives compared to other approaches. The proposed system is fully automated with fast processing speed. The studies have shown a high potential for implementation of this system in clinical practice as a CAD tool.
Lin
Daw-Tung
DT
Department of Computer Science and Information Engineering, National Taipei University, 151, University Road, San Shia, Taipei, Taiwan 237, ROC. dalton@chu.edu.tw
Yan
Chung-Ren
CR
Chen
Wen-Tai
WT
D000465:Algorithms; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*