Detection of pulmonary nodules based on a multiscale feature 3D U-Net convolutional neural network of transfer learning.

ABSTRACT: A new computer-aided detection scheme is proposed, the 3D U-Net convolutional neural network, based on multiscale features of transfer learning to automatically detect pulmonary nodules from the thoracic region containing background and noise. The test results can be used as reference information for doctors to assist in the detection of early lung cancer. The proposed scheme is composed of three major steps: First, the pulmonary parenchyma area is segmented by various methods. Then, the 3D U-Net convolutional neural network model with a multiscale feature structure is built. The network model structure is subsequently fine-tuned by the transfer learning method based on weight, and the optimal parameters are selected in the network model. Finally, datasets are extracted to train the fine-t

SUBMITTER: Tang S 

PROVIDER: S-EPMC7449493 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature