The application of series multi-pooling convolutional neural networks for medical image segmentation
It is crucial to precisely classify the pixels in brain tumor tissues in the brain tumor image segmentation. However, the traditional segmentation method is somewhat restricted and the segmentation accuracy cannot meet the real requirements because of the randomness of brain tumors’ spatial location...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717748899 |
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| _version_ | 1832547811374137344 |
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| author | Feng Wang Siwei Huang Lei Shi Weiguo Fan |
| author_facet | Feng Wang Siwei Huang Lei Shi Weiguo Fan |
| author_sort | Feng Wang |
| collection | DOAJ |
| description | It is crucial to precisely classify the pixels in brain tumor tissues in the brain tumor image segmentation. However, the traditional segmentation method is somewhat restricted and the segmentation accuracy cannot meet the real requirements because of the randomness of brain tumors’ spatial location in the brain. To solve the said problems, the model of convolutional neural network in the deep learning approach was used in this article to cope with classification and labeling tasks of brain tumor images. The main contents of this article were studied as follows: the principle and operating approach of convolutional neural network on image processing was first introduced, and then 12-layer convolutions were skillfully set up for local pathways based on two-way convolutional neural network architectures; considering the inter-label dependency in pixel areas, the situation of conditional random field was simulated to design the input series connection structure; multi-pooling input series connection model was designed to solve the problem that the input pixel area is limited; finally, the classification accuracy upon experiments reached 83%, which has verified the effectiveness of model to improve. |
| format | Article |
| id | doaj-art-d539f639081e446cb0eb802208dd7245 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-d539f639081e446cb0eb802208dd72452025-02-03T06:43:17ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-12-011310.1177/1550147717748899The application of series multi-pooling convolutional neural networks for medical image segmentationFeng Wang0Siwei Huang1Lei Shi2Weiguo Fan3College of Information Engineering, Taiyuan University of Technology, Taiyuan, P.R. ChinaCollege of Information Engineering, Taiyuan University of Technology, Taiyuan, P.R. ChinaCollege of Information Engineering, Taiyuan University of Technology, Taiyuan, P.R. ChinaVirginia Polytechnic Institute and State University, Blacksburg, VA, USAIt is crucial to precisely classify the pixels in brain tumor tissues in the brain tumor image segmentation. However, the traditional segmentation method is somewhat restricted and the segmentation accuracy cannot meet the real requirements because of the randomness of brain tumors’ spatial location in the brain. To solve the said problems, the model of convolutional neural network in the deep learning approach was used in this article to cope with classification and labeling tasks of brain tumor images. The main contents of this article were studied as follows: the principle and operating approach of convolutional neural network on image processing was first introduced, and then 12-layer convolutions were skillfully set up for local pathways based on two-way convolutional neural network architectures; considering the inter-label dependency in pixel areas, the situation of conditional random field was simulated to design the input series connection structure; multi-pooling input series connection model was designed to solve the problem that the input pixel area is limited; finally, the classification accuracy upon experiments reached 83%, which has verified the effectiveness of model to improve.https://doi.org/10.1177/1550147717748899 |
| spellingShingle | Feng Wang Siwei Huang Lei Shi Weiguo Fan The application of series multi-pooling convolutional neural networks for medical image segmentation International Journal of Distributed Sensor Networks |
| title | The application of series multi-pooling convolutional neural networks for medical image segmentation |
| title_full | The application of series multi-pooling convolutional neural networks for medical image segmentation |
| title_fullStr | The application of series multi-pooling convolutional neural networks for medical image segmentation |
| title_full_unstemmed | The application of series multi-pooling convolutional neural networks for medical image segmentation |
| title_short | The application of series multi-pooling convolutional neural networks for medical image segmentation |
| title_sort | application of series multi pooling convolutional neural networks for medical image segmentation |
| url | https://doi.org/10.1177/1550147717748899 |
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