title: A new method based on MTANNs for cutting down false-positives: an evaluation on different versions of commercial pulmonary nodule detection CAD software.
abstract: 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.
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, China.
authors.forename: Zhenghao
authors.initials: Z
authors.lastname: Shi
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, China.
authors.forename: Chunjiao
authors.initials: C
authors.lastname: Si
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, China.
authors.forename: Yaning
authors.initials: Y
authors.lastname: Feng
authors.affiliation: School of Information Science and Technology, Aichi Prefectural University, Nagakute, Aichi 4801198, Japan.
authors.forename: Lifeng
authors.initials: L
authors.lastname: He
authors.affiliation: Department of Radiology, University of Chicago, Chicago, IL 60637, USA.
authors.forename: Kenji
authors.initials: K
authors.lastname: Suzuki
mesh_terms: D000465:Algorithms; D005189:False Positive Reactions; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D012986:Software Validation; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*
keywords: Commercial CAD; False Positive; Mixture of MTANNs; cutting down