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Proceedings Paper

Effect of a small number of training cases on the performance of massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT
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Paper Abstract

In this study, we investigated a pattern-classification technique which can be trained with a small number of cases with a massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT (LDCT). The MTANN consists of a modified multilayer artificial neural network (ANN), which is capable of operating on image data directly. The MTANN is trained by use of a large number of sub-regions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule." The output image is obtained by scanning of an input image with the MTANN. In the MTANN, the distinction between nodules and non-nodules is treated as an image-processing task, in other words, as a highly nonlinear filter that performs both nodule enhancement and non-nodule suppression. This allows us to train the MTANN not on a case basis, but on a sub-region basis. Therefore, the MTANN can be trained with a very small number of cases. Our database consisted of 101 LDCT scans acquired from 71 patients in a lung cancer screening program. The scans consisted of 2,822 sections, and contained 121 nodules including 104 nodules representing confirmed primary cancers. With our current CAD scheme, a sensitivity of 81.0% (98/121 nodules) with 0.99 false positives per section (2,804/2,822) was achieved. By use of the MTANN trained with a small number of training cases (n=10), i.e., five pairs of nodules and non-nodules, we were able to remove 55.8% of false positives without a reduction in the number of true positives, i.e., a classification sensitivity of 100%. Thus, the false-positive rate of our current CAD scheme was reduced from 0.99 to 0.44 false positive per section, while the current sensitivity (81.0%) was maintained.

Paper Details

Date Published: 15 May 2003
PDF: 12 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480181
Show Author Affiliations
Kenji Suzuki, Univ. of Chicago (United States)
Samuel G. Armato, Univ. of Chicago (United States)
Feng Li, Univ. of Chicago (United States)
Shusuke Sone, Azumi General Hospital (Japan)
Kunio Doi, Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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