Abstract：
:Due to insufficient samples, the generalization performance of deep network is insufficient. In order to solve this problem, an improved U-net based image automatic segmentation and diagnosis algorithm was proposed, in which the max-pooling operation in original U-net model was replaced by the convolution operation to keep more feature information. Firstly, the regions of 128×128 were extracted from all slices of the patients as data samples. Secondly, the patient samples were divided into training sample set and testing sample set, and data augmentation was performed on the training samples. Finally, all the training samples were adopted to train the model. Compared with Fully Convolutional Network (FCN) model and max-pooling based U-net model, DSC and CR coefficients of the proposed method achieve the best results, while PM coefficient is 2.55 percentage lower than the maximum value in the two comparison models, and Average Symmetric Surface Distance is slightly higher than the minimum value of the two comparison models by 0.004. The experimental results show that the proposed model can achieve good segmentation and diagnosis results.
journal_name
J Med Systjournal_title
Journal of medical systemsauthors
Li Q,Fan S,Chen Cdoi
10.1007/s10916-019-1432-0subject
Has Abstractpub_date
2019-08-12 00:00:00pages
304issue
9eissn
0148-5598issn
1573-689Xpii
10.1007/s10916-019-1432-0journal_volume
43pub_type
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journal_title：Journal of medical systems
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