TY - GEN
T1 - Pulmonary DR Image Anomaly Detection Based on Deep Learning
AU - Song, Zhendong
AU - Fan, Lei
AU - Huang, Dong
AU - Feng, Xiaoyi
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - The morbidity and mortality in lung cancer is increasing which makes the diagnosis of abnormal lungs particularly important. Because of the advantages in DR image, this paper aimed at two problems in current medical image research: first, it is difficult to completely segment the lung of DR image only used traditional image segmentation methods. This paper replaces the padding in the U-net network model with zero padding to maintain the image size and apply it to the lung DR image segmentation, and finally uses the lung DR image dataset to fine-tuning. Secondly, the results of anomaly detection experiments show that the algorithm would get more complete segmentation of lung DR images. Secondly, because of the insufficient of training set, the idea of multi-classifier fusion is used. Combining Gabor-based SVM classification, 3D convolutional neural network, and transfer learning to achieve a more complete description of features and make full use of the classification advantages of multi-classifiers. The experimental results show that the classification accuracy of this algorithm is 6% higher than that of the Transfer-ImageNet algorithm, 5% higher than SVM, 15% higher than 3D convolutional neural network, and improved 2.5% compared with FT-Transfer-DenseNet3D algorithm.
AB - The morbidity and mortality in lung cancer is increasing which makes the diagnosis of abnormal lungs particularly important. Because of the advantages in DR image, this paper aimed at two problems in current medical image research: first, it is difficult to completely segment the lung of DR image only used traditional image segmentation methods. This paper replaces the padding in the U-net network model with zero padding to maintain the image size and apply it to the lung DR image segmentation, and finally uses the lung DR image dataset to fine-tuning. Secondly, the results of anomaly detection experiments show that the algorithm would get more complete segmentation of lung DR images. Secondly, because of the insufficient of training set, the idea of multi-classifier fusion is used. Combining Gabor-based SVM classification, 3D convolutional neural network, and transfer learning to achieve a more complete description of features and make full use of the classification advantages of multi-classifiers. The experimental results show that the classification accuracy of this algorithm is 6% higher than that of the Transfer-ImageNet algorithm, 5% higher than SVM, 15% higher than 3D convolutional neural network, and improved 2.5% compared with FT-Transfer-DenseNet3D algorithm.
KW - 3D convolutional neural network
KW - DR image
KW - Lung field segmentation
KW - SVM
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85076922407&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-34120-6_15
DO - 10.1007/978-3-030-34120-6_15
M3 - 会议稿件
AN - SCOPUS:85076922407
SN - 9783030341190
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 182
EP - 198
BT - Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 1
A2 - Zhao, Yao
A2 - Lin, Chunyu
A2 - Barnes, Nick
A2 - Chen, Baoquan
A2 - Westermann, Rüdiger
A2 - Kong, Xiangwei
PB - Springer
T2 - 10th International Conference on Image and Graphics, ICIG 2019
Y2 - 23 August 2019 through 25 August 2019
ER -
