Open AccessA new method based on MTANNs for cutting down false-positives: An evaluation on different versions of commercial pulmonary nodule detection CAD software
Author(s) -
Zhongchao Shi,
Chunjiao Si,
Yu Feng,
Lifeng He,
Kenji Suzuki
Publication year - 2014
Publication title -
bio-medical materials and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.372
H-Index - 53
eISSN - 1878-3619
pISSN - 0959-2989
DOI - 10.3233/bme-141102
Subject(s) - false positive paradox , sigmoid function , cad , computer science , software , artificial neural network , radiography , computer aided diagnosis , artificial intelligence , pattern recognition (psychology) , radiology , medicine , engineering drawing , engineering , programming language
One of the major problems for computer-aided pulmonary nodule detection in chest radiographs is that a high false-positive (FP) rate exists. In an effort to overcome this problem, a new method based on the MTANN (Massive Training Artificial Neural Network) is proposed in this paper. An MTANN comprises a multi-layer neural network where a linear function rather than a sigmoid function is used as its activity function in the output layer. In this work, a mixture of multiple MTANNs were employed rather than only a single MTANN. 50 MTANNs for 50 different types of FPs were prepared firstly. Then, several effective MTANNs that had higher performances were selected to construct the MTANNs mixture. Finally, the outputs of the multiple MTANNs were combined with a mixing neural network to reduce various different types of FPs. The performance of this MTANNs mixture in FPs reduction is validated on three different versions of commercial CAD software with a validation database consisting of 52 chest radiographs. Experimental results demonstrate that the proposed MTANN approach is useful in cutting down FPs in different CAD software for detecting pulmonary nodules in chest radiographs.