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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Evaluation of MTANNs for eliminating false-positive with different computer aided pulmonary nodules detection
Zhenghao Shi1, Jiejue Ma2, Yaning Feng1
1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.
Abstract:
MTANN (Massive Training Artificial Neural Network) is a promising tool, which applied to eliminate false-positive for thoracic CT in recent years. In order to evaluate whether this method is feasible to eliminate false-positive of different CAD schemes, especially, when it is applied to commercial CAD software, this paper evaluate the performance of the method for eliminating false-positives produced by three different versions of commercial CAD software for lung nodules detection in chest radiographs. Experimental results demonstrate that the approach is useful in reducing FPs for different computer aided lung nodules detection software in chest radiographs.

