Pulmonary DR Image Anomaly Detection Based on Deep Learning

Zhendong Song, Lei Fan, Dong Huang, Xiaoyi Feng

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 1
编辑Yao Zhao, Chunyu Lin, Nick Barnes, Baoquan Chen, Rüdiger Westermann, Xiangwei Kong
出版商Springer
182-198
页数17
ISBN(印刷版)9783030341190
DOI
出版状态已出版 - 2019
活动10th International Conference on Image and Graphics, ICIG 2019 - Beijing, 中国
期限: 23 8月 201925 8月 2019

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11901 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议10th International Conference on Image and Graphics, ICIG 2019
国家/地区中国
Beijing
时期23/08/1925/08/19

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