False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network.
OBJECTIVE
We developed a technique that uses a multiple massive-training artificial neural network (multi-MTANN) to reduce the number of false-positive results in a computer-aided diagnostic (CAD) scheme for detecting nodules in chest radiographs.


METHODS
Our database consisted of 91 solitary pulmonary nodules, including 64 malignant nodules and 27 benign nodules, in 91 chest radiographs. With our current CAD scheme based on a difference-image technique and linear discriminant analysis, we achieved a sensitivity of 82.4%, with 4.5 false positives per image. We developed the multi-MTANN for further reduction of the false positive rate. An MTANN is a highly nonlinear filter that can be trained with input images and corresponding teaching images. To reduce the effects of background levels in chest radiographs, we applied a background-trend-correction technique, followed by contrast normalization, to the input images for the MTANN. For enhancement of nodules, the teaching image was designed to contain the distribution for a "likelihood of being a nodule." Six MTANNs in the multi-MTANN were trained by using typical nodules and six different types of non-nodules (false positives).


RESULTS
Use of the trained multi-MTANN eliminated 68.3% of false-positive findings with a reduction of one true-positive result. The false-positive rate of our original CAD scheme was improved from 4.5 to 1.4 false positives per image, at an overall sensitivity of 81.3%.


CONCLUSIONS
Use of a multi-MTANN substantially reduced the false-positive rate of our CAD scheme for lung nodule detection on chest radiographs, while maintaining a level of sensitivity.
Suzuki
Kenji
K
Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA. suzuki@uchicago.edu
Shiraishi
Junji
J
Abe
Hiroyuki
H
MacMahon
Heber
H
Doi
Kunio
K
D005189:False Positive Reactions; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000175:diagnosis*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D012680:Sensitivity and Specificity