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

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.