Sanders CT採用PCANet和數據增強技術對跟骨骨折圖像進行分類

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姓名

方達(Nurya Aghnia Farda) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

Sanders CT採用PCANet和數據增強技術對跟骨骨折圖像進行分類
(Sanders CT Classification of Calcaneal Fracture Images using PCANet and Data Augmentation Techniques)

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摘要(中)

跟骨骨折占t骨?域所有成人骨折的60％以上。建?确定患者是否患有跟骨骨折可能非常具有挑?性。?生通常??行X光和CT?查，并?患者?行?外的?查。?了?予最佳治?，整形外科需要知道跟骨骨折的?型。然而，即使在CT?像的?助下，它?仍然成?确定骨折?型的??。
除此之外，机器??和深度??已???增?，它能?解??多不同?域的复?任?。在生物???域，它已被用于????像中??和分?癌??。在??研究中，我??PCANet?用于深度神?网?架构。
此外，由于患有跟骨骨折CT?像的患者?量?少，影??据集的?量，我?采用?据增?技??????段生成新的?据集。我?的?察表明，???添加增??据可以??提高??准确性。
最后，我?提出了一?初步研究，?容涉及建立健?的跟骨骨折??系?所涉及的可能性，机遇和挑?。我?的???果表明，我?提出的方法可以?到高?70％的??精度。

摘要(英)

Calcaneus fracture has contributed to more than 60% of all adult fractures on the tarsal bones area. It is suggested that determining if a patient is having a calcaneal fracture can be very challenging. Doctors usually will perform an X-Ray and CT examination and doing additional tests with the patients. In order to give the best treatment, orthopedics need to know the type of the calcaneal fracture. However, it is still becoming a problem for them to determine the fracture type even with the help of CT images.
Apart from that, machine learning and deep learning has grown significantly that it is able to solve complex tasks in many different fields. In biomedical fields, it has been used to detect and classify cancer tissues from medical images. In this research, we apply PCANet for the deep neural network architecture.
Also, due to the small number of patients having the calcaneal fracture CT images which affect on the number of dataset, we employ a data augmentation technique to generate new dataset for the training phase. Our observation shows that the addition of augmented data for training can increase the detection accuracy significantly.
In the end, we present a preliminary study regarding the possibility, opportunities, and challenges which are involved in building a robust calcaneal fracture detection system. Our experimental results show that our proposed approach can reach up to 70% of detection accuracy.

關鍵字(中)

★ 卷積神經網絡
★ PCANet
★ 跟骨骨折檢測
★ 圖像增強

關鍵字(英)

★ Convolutional Neural Network
★ PCANet
★ calcaneus fracture detection
★ image augmentation

論文目次

1 CHAPTER 1 1
1.1 Background 1
2 CHAPTER 2 3
2.1 Medical Images Evolution 3
2.1.1 Computed Tomography Scan Images 3
2.2 Calcaneus 5
2.2.1 Calcaneus Fracture 8
2.3 Sanders Classification 10
2.4 Image Processing 12
2.5 Machine Learning and Deep Learning 14
2.5.1 Deep Learning in Medical Imaging 15
2.6 PCANet 16
2.7 Image Augmentation 18
3 CHAPTER 3 22
3.1 System architecture 22
3.2 Dataset 23
3.2.1 Data preparation: Dataset selection and labelling 24
3.2.2 Image Processing 28
3.2.3 Image Augmentation 30
3.3 Experiment designs 32
3.3.1 Experiment 1: feasibility of the proposed approach 32
3.3.2 Experiment 2: feasibility of the proposed approach using augmented training images 34
3.4 Performance Evaluation 34
4 Experiments and Results 35
4.1 Experiment 1: feasibility of the proposed approach 35
4.1.1 Experiment 1.1 35
4.1.2 Experiment 1.2 36
4.2 Experiment 2: feasibility of the proposed approach using augmented training images 38
5 CONCLUSIONS AND FUTURE WORKS 41
5.1 Conclusions 41
5.2 Future Works 41
6 References 42

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指導教授

王家慶(Wang, Jia-Ching)

審核日期

2018-7-26

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