Applications of Deep Learning in Medical Image Processing
| Autor: | Chia-Mei Tang, 唐嘉梅 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 In recent years, deep learning is a very hot topic. People can solve a lot of problem with deep learning. This paper hopes to apply deep learning in medical area, hoping to help doctors save their judgment time and reduce misjudgment rate. Thus doctors can help patient to find diseases earlier and treat it earlier. This paper uses Convolutional Neural Network to build a system that can automatically detect calcified plaque in cardiac blood vessels. The system has three main features. First, extract cardiac blood vessels images by using image processing methods. Second, use Convolutional Neural Network to train a calcified plaque detection model. Third, locate calcified plaque by using image processing methods. Moreover, this paper also builds another system for detecting vertebral compression fractures by Mask R-CNN. The system has two major features. First, train a model which can separate spines images from vertebral medical images by using Mask R-CNN. Second, train another model to detect the four vertex by using Mask R-CNN. Finally using these results to do the vertebral compression fractures detection. The cardiac blood vessels extraction algorithm of this system has succeeded extract 87% of cardiac blood vessels completely, and the accuracy of calcified plaque detection almost reach 80%. The misjudgment is about 0.21 calcified plaques per image. And also location accuracy of calcified plaque is more than 70%. For these tests proves that the method we used in this paper can efficiently find the calcified plaque on cardiac blood vessels. The segmentation of vertebral images algorithm can separate 97% or more of vertebral bones images, and also find 6 vertices of each vertebral bones images correctly. 70% or more of vertebral compression fractures has been detected by the system. These results indicate that the system can assist doctors to detect vertebral compression fractures efficiently. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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