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研究生:孟瓏承
研究生(外文):MENG,LONG-CHENG
論文名稱:基於生成對抗網路之腦腫瘤影像分類深度學習模型
論文名稱(外文):Generative adversarial networks(GAN) based deep learning model for brain tumors classification
指導教授:詹前隆詹前隆引用關係
指導教授(外文):CHIEN-LUNG CHAN
口試委員:簡廷因許嘉裕李愛先
口試委員(外文):TING-YING CHIENCHIA-YU HSUAI-HSIEN LI
口試日期:2022-06-28
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:81
中文關鍵詞:智慧醫療輔助診斷影像辨識深度學習大數據分析
外文關鍵詞:Smart healthcarepredictive analysisimage recognitiondeep learningbig data analysis
相關次數:
  • 被引用被引用:0
  • 點閱點閱:342
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  • 下載下載:65
  • 收藏至我的研究室書目清單書目收藏:0
近年來智慧醫療(Wise Information Technology of Med, WITMED)成為全球醫療發展趨勢,其中最重要的是醫學影像判讀,能夠使用醫療影像進行分析與預測,藉由相關的大數據分析,包含電腦斷層影像(computed tomography, CT)、心電圖(Electrocardiography, EGG)、醫學超音波(Medical ultrasound)、磁共振成像(Magnetic resonance imaging, MRI)等,來分類病患是否確診,達到醫療輔助診斷之效果,有效的降低誤診發生率與死亡率。本研究針對醫療影像的分類提出一個基於深度學習之方法,使用腦腫瘤MRI成像加以分析,藉由深度學習及大數據分析技術,使用生成對抗網路(Generative Adversarial Network, GAN)解決醫療數據缺乏之問題,並透過卷積神經網路(Convolutional Neural Network, CNN)、視覺轉換器(vision transformer, ViT)等多種網路模型進行醫療影像分類研究,輔助醫生進行診斷,提供醫師建議,降低誤判風險,進而達到精準醫療願景。本研究實驗結果表明,使用生成對抗網路模型能夠有效改善數據缺乏問題,並且用於醫療影像分類之深度學習模型能有效的分類腦部腫瘤疾病。
Recently, the Wise Information Technology of Med (WITMED) has become a global medical development trend. In which, the medical image interpretation is the most important application using medical images for analysis and prediction including relevant big data analysis such as computed tomography(CT), electrocardiography(EGG), medical ultrasound (Medical ultrasound), Magnetic resonance imaging (MRI), etc., This system aims to classify whether the patient has the disease, as a result, achieving the effect of medical auxiliary diagnosis, and effectively reducing the incidence of misdiagnosis and mortality. Accordingly, this study proposes a deep learning-based method for the classification task of brain tumor MRI images. In detail, the Generative Adversarial Network (GAN) is deployed to balance the dataset. After that, various DL models are deployed for classification tasks such as Convolutional Neural Network (CNN) and Vision Transformer (ViT). This proposed method becomes an objective assistant for physicians in advising and diagnosing, as a consequence, reducing the risk of misjudgment, and then achieving Precision Medicine Vision. The experiment result shows that using GAN can improve the performance of classification models by balancing the distribution ratio among classes. Furthermore, the DL models perform well in the medical image for classifying diseases.

目錄
書名頁 i
論文口試委員審定書 ii
中文摘要 iii
英文摘要 iv
誌謝 v
目錄 vi
表目錄 ix
圖目錄 xi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究流程 3
第二章 文獻探討 6
2.1 輔助醫療 6
2.2 腦部腫瘤 7
2.3 醫療數據缺乏 8
2.4 深度學習 9
2.4.1 深度學習應用於醫療 10
2.4.2 深度學習應用於視覺化 11
2.4.3 卷積神經網路 12
2.4.4 生成對抗網路 16
2.4.5 Vision Transformer(ViT) 18
2.4.6 深度殘差網路 19
2.4.7 VGG16 21
第三章 研究方法 23
3.1 研究流程 23
3.2 研究資料 25
3.2.1腦腫瘤MRI數據集(Brain Tumor MRI Dataset) 25
3.2.2資料處理 27
3.3 深度學習模型 28
3.3.1 卷積神經網路 28
3.3.2 Projected GAN 31
3.3.3 Vision Transformer(ViT) 35
3.3.4 深度殘差網路 37
3.3.5 VGG16 39
3.3.6 激勵函數(Activation Function) 41
3.4 模型評估指標 43
3.5 實驗環境 45
第四章 研究結果 46
4.1 資料集處理 46
4.1.1 原始資料 46
4.1.2 資料增強後資料 47
4.2 實驗設計與模型參數設定 50
4.2.1 VGG模型架構 51
4.2.2 ResNet模型架構 51
4.2.3 ViT 模型架構 52
4.3 模型評估 53
4.3.1 VGG的訓練結果 54
4.3.2 ResNet的訓練結果 58
4.3.3 ViT 的訓練結果 62
4.3.4錯誤的分類樣本觀察 66
4.3.5 結果比較 66
第五章 結論與建議 70
5.1 結論 70
5.2 建議與未來發展方向 71
參考文獻 72


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