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系統識別號 U0026-2708202015524900
論文名稱(中文) 建構高齡者失智症之智慧專家系統之研究
論文名稱(英文) Developing AI Expert System on Dementia for the Elderly
校院名稱 成功大學
系所名稱(中) 工業設計學系
系所名稱(英) Department of Industrial Design
學年度 108
學期 2
出版年 109
研究生(中文) 張駿揚
研究生(英文) Chun-Yang Chang
學號 P36071074
學位類別 碩士
語文別 英文
論文頁數 138頁
口試委員 指導教授-林彥呈
指導教授-陳建旭
口試委員-陳芃婷
口試委員-洪煒斌
中文關鍵字 阿茲海默症  單光子電腦斷層掃描  影像辨識  深度學習  神經網路 
英文關鍵字 Alzheimer disease  Positron Emission Tomography  Image Recognition  Deep learning  Neural network 
學科別分類
中文摘要 隨著高齡化社會的來臨,阿茲海默症的患病人數逐年攀升。但是,目前尚未有有效的方法與藥物治療來減緩疾病的發生。近年來,神經影像學正逐漸成為了解阿茲海默症疾病進程的有益方式,並且在醫學成像技術領域中使用深度學習技術的重要性在研究和臨床上都有很大的進展。使得許多研究者紛紛投入發展以醫學影像作為判斷的輔助診斷系統。另一方面,由於具有普遍性、低成本和非侵入性的特性,單光子電腦斷層掃描(SPECT)在神經影像學中佔據優勢,且確實在很大程度上呈現了阿茲海默症的診斷特徵。然而,SPECT影像的診斷需要嚴謹的過程同時必須具備高水平的專業知識。醫師需要花上大量時間查看影像以進行診斷。因此,深度學習能夠通過對特徵的自動檢測來幫助神經科醫師在臨床上的診斷。讓醫師除了可以依靠自己臨床的經驗外,也可以透過輔助系統的分辨結果做為客觀的參考的依據。從而減低了因為人對事物判斷的侷限性所帶來的失誤。
本研究旨在發展能夠分析阿茲海默疾病嚴重程度的AI專家系統,藉由偵測腦部特定區域的血流量,即時分析出受測者阿茲海默症的嚴重程度,並依照嚴重程度即時分析出該名患者的疾病嚴重程度狀況。提出了一個利用深度學習特性的方法使SPECT影像在判讀上能有較高的辨識準確率。採用近期受到廣泛應用的AutoKeras方式基於殘差網路架構,重新建構了一個深度神經網路,並使用來自成大醫院神經科提供的影像數據集對模型進行訓練。本研究成功對阿茲海默症受試者的功能性SPECT數據進行了分類,驗證數據和測試數據的準確性分別達到83.17%和76.39%。實驗結果表明,所提出的深度神經網絡方法能夠有效地利用SPECT影像診斷阿茲海默症,並清楚地解釋了所提出方法的有效性。期望能夠促進疾病輔助診斷技術的進步。幫助神經科醫師於診斷阿茲海默症時,可以做正確的判斷。
英文摘要 With the advent of an aging society, the number of people who are being afflicted with Alzheimer’s disease is also on a gradual rise. However, an effective medical treatment to contain the contraction of the disease still does not exist. Neuroimaging is becoming a progressively beneficial method in understanding the pathogenesis of AD progress over the recent years, and the importance of using deep learning techniques in the field of medical imaging technologies have made progress in both research and clinical care. Many researchers have engaged in the development of a auxiliary diagnostic system for medical imaging. On the other hand, with the features of being prevalent, inexpensive and non-invasive, SPECT has the whip hand of Neuroimaging and does present diagnostic features of AD to a great extent. However, diagnosis of SPECT images requires a rigorous process and must have a high level of expertise. Physicians need to spend a lot of time viewing images to make a diagnosis. Therefore, deep learning can assist neurologists diagnose clinically by automatic detection of representations. In addition to relying on their own clinical experience, physicians can also use the discrimination results of the auxiliary system as the basis for objective reference and thus to reduce the mistakes caused by the limitations of human judgment.
The study will develop an AI expert system that can analyze the severity of Alzheimer's disease. By detecting blood flow in specific areas of the brain, the severity of Alzheimer's disease in the subject can be analyzed in real time. A method that utilizes deep learning features to enable SPECT images to have a higher recognition accuracy in detection were proposed in this research. We use the AutoKeras which is widely implied to reconstruct a deep neural network based on the residual network architecture, and the model was trained by the image datasets provided from the Department of Neurology, National Cheng Kung University Hospital. The study successfully classified the functional SPECT data of subjects with Alzheimer’s disease, where the accuracy of validation data and testing data respectively reached 83.17% and 76.39%. Experimental results demonstrate that the proposed method of deep neural network is efficient for the diagnosis of Alzheimer's disease with SPECT images, and clearly interprets the effectiveness of the proposed method. It is expected to facilitate technological advancements in disease-aided diagnosis and help neurologists make correct judgments in the diagnosis of Alzheimer's disease.
論文目次 摘要 i
abstract ii
ACKNOWLEDGEMENTS iv
TABLE OF CONTENTS v
LIST OF TABLES viii
LIST OF FIGURES ix
LIST OF SYMBOLS AND ABBREVIATIONS xi
CHAPTER 1 INTRODUCTION 1
1.1 Research Background 1
1.1.1 Facts about Alzheimer's Disease 2
1.1.2 The Relationship between Deep Learning and the Medical Field 5
1.1.3 Application of Image Recognition in Medical Field 7
1.2 Research Motivation 8
1.3 Research Purpose 11
1.4 Related research 12
1.5 Research Scope 13
1.6 Research Framework 15
1.6.1 Results of The Phase 1 Research 17
1.6.2 Results of The Phase 2 Research 18
1.7 Research Contribution 19
1.7.1 Academic Research Contribution 19
1.7.2 Practical Research Contribution 19
CHAPTER 2 Literature review 21
2.1 Academic Knowledge of Alzheimer's Disease 21
2.1.1 Alzheimer's Disease 21
2.1.2 Symptoms 22
2.1.3 Regional Cerebral Blood Flow (rCBF) 24
2.1.4 Diagnosis 25
2.1.5 Single Photon Emission Computed Tomography (SPECT) 27
2.1.6 The Importance of Accurate Diagnosis 29
2.1.7 Summary 30
2.2 Artificial Intelligence 30
2.2.1 Neural Networks 31
2.2.2 Convolutional Neural Networks 32
2.2.3 The Development of CNN 35
2.2.4 Residual Network 38
2.2.5 Deep Learning 42
2.2.6 Transfer Learning 42
2.2.7 Summary 44
2.3 Program Library 44
2.3.1 Python 44
2.3.2 Tensorflow 46
2.3.3 Keras 47
2.3.4 Auto-Keras 48
CHAPTER 3 Methodology 50
3.1 Phase 1 Research 52
3.1.1 Document Analysis 52
3.1.2 Expert Interview 55
3.1.3 Data Acquisition and Classification 60
3.2 Phase 2 Research 61
3.2.1 Image Processing 62
3.2.2 Establishment of Neural Network Model 64
3.2.3 Workflow Overview 65
3.2.4 Interface Erection 66
CHAPTER 4 results and discussion 68
4.1 Experimental Environment 68
4.1.1 Research Software and Equipment 68
4.1.2 Source of Image Data 69
4.1.3 Experimental setup 71
4.2 Neural Network Architecture 72
4.3 Experiment Procedure 73
4.4 Classification performance 75
4.5 Graphical User Interface 77
4.5.2 System Operating Mode 79
4.5.3 Interface operation flow 80
4.6 Discussion 82
CHAPTER 5 conclusion 85
5.1 Research Limitation 86
5.2 Future prospect 87
5.2.1 Future Applications 89
REFERENCES 90
Appendix A EXPERT INTERVIEW 101
Appendix b Experiment Procedure 104
B.1 MobileNetV2 106
B.2 NasNetMobile 109
B.3 VGG16 114
B.4 Inception V3 118
B.5 ResNet 122
Appendix c additional materials 127
C.1 Motivation 127
C.2 Pathological Cause 130
C.3 Positron Emission Tomography (PET) 131
C.4 Early Alzheimer’s Diagnosis 132
C.5 Object Detection 133
C.5.1 Faster with Convolutional Neural Networks Features 133
C.5.2 You Only Look Once 135
C.5.3 Single Shot Detector 136

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