JSAI2020
Jun 9 - Jun 12, 2020Virtual Meetings
JSAI2020
Jun 9 - Jun 12, 2020Virtual Meetings
[1C5-GS-13-05]Lung Nodule Detection Using 3D Convolutional Neural Network
〇Taku Ri1, Tatsuya Yamazaki2(1. Graduate School of Niigata University, 2. Niigata University)
Keywords:
Deep Learning,3D Convolutional Neural Network,Computer Tomography Image
In this paper, we propose a method for detecting nodules that cause cancer from three-dimensional (3D) computer tomography (CT) images of the lung field. In the proposed method, a 3D image extracted from the lung field of a CT image is input to a model constructed by a 3D convolutional neural network. Then, when the input small area image is determined to be a nodule, a mark is labelled at the corresponding position in the CT image. In this paper, in order to verify the effectiveness of the model in the proposed method, the classification accuracy is verified using a model constructed with two classes, nodules and non-nodules, and then several CT images were used to detect nodules. As a result, the accuracy rate of the model was 94.44%. However, some false-positives were confirmed by nodules detection.