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姓名 周大豐(Da-Feng Chou) 查詢紙本館藏 畢業系所 數學系 論文名稱 利用遷移式學習對於腦部MRI影像之阿茲海默症疾病分類
(Classification of Alzheimer′s disease in brain MRI images using transfer learning)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 本研究旨在利用深度學習方法對阿茲海默症患者進行分類，探索分析不同深度學
習模型在分類任務中的表現。
本研究收集了來自醫療公開數據庫來源的MRI影像數據，並使用多種深度學習方
法進行分析。具體而言，我們應用了Keras提供的卷積神經網路（CNN）的預訓練模
型，以提高分類的準確性和穩定性。在數據預處理階段，我們對數據進行了標準化處
理，並且用OpenCV此套件對數據影像進行切割腦室與海馬體。實驗結果表明，基於
深度學習的方法在阿茲海默症分類中具有顯著的優勢，能夠有效區分阿茲海默症患者。摘要(英) With the acceleration of global aging, Alzheimer’s Disease (AD) has become a signifi
cant public health issue. Accurate classification of AD is crucial for developing personalized
treatment plans and prognosis evaluations.
This study aims to classify Alzheimer’s disease patients by using deep learning methods
and explore the performance of different deep learning models in classification tasks.
The study collected MRI image data from publicly available medical databases and an
alyzed it by using various deep learning methods. We applied pre-trained convolutional neu
ral network (CNN) models provided by Keras to improve classification accuracy and stability.
During the data preprocessing stage, we standardized the data and used the OpenCV library to
segment the ventricles and hippocampus in the images. The experimental results indicate that
deep learning-based methods have significant advantages in Alzheimer’s disease classification
and can effectively distinguish Alzheimer’s disease patients.關鍵字(中) ★ 深度學習
★ 卷積神經網路
★ 預訓練模型
★ 阿茲海默症關鍵字(英) 論文目次 摘要i
Abstract ii
誌謝iii
目錄iv
一、緒論1
二、文獻探討2
三、研究工具4
3.1卷積神經網路................................................................. 4
3.1.1卷積運算............................................................... 4
3.1.2池化運算............................................................... 5
3.1.3激活函數............................................................... 6
3.2 Adam優化器.................................................................. 6
3.2.1理論基礎............................................................... 6
3.2.2 Adam演算法........................................................... 7
四、資料來源與資料前處理9
4.1資料來源...................................................................... 9
4.2資料前處理.................................................................... 9
五、模型選取15
5.1卷積神經網路預訓練模型..................................................... 15
5.2自定義全連接層............................................................... 16
5.3自定義架構.................................................................... 16
5.4實驗設計...................................................................... 18
六、實驗結果19
6.1績效衡量標準................................................................. 19
6.2健康大腦與輕度癡呆分類..................................................... 20
6.2.1預訓練模型結果比較.................................................. 21
6.3健康大腦與和有阿茲海默症大腦分類........................................ 23
6.3.1預訓練模型結果比較.................................................. 23
6.4將輕度癡呆與阿茲海默症均視為陽性，與健康大腦做二元分類............. 25
七、總結27
參考文獻28參考文獻 [1] M. Liu, F. Li, H. Yan, et al., “A multi-model deep convolutional neural network for automatic
hippocampus segmentation and classification in alzheimer＇s disease,” Neuroimage, vol. 208,
p. 116459, 2020.
[2] R. A. Morey, C. M. Petty, Y. Xu, et al., “A comparison of automated segmentation and manual
tracing for quantifying hippocampal and amygdala volumes,” Neuroimage, vol. 45, no. 3, pp. 855
866, 2009.
[3] N. An, H. Ding, J. Yang, R. Au, and T. F. Ang, “Deep ensemble learning for alzheimer’s disease
classification,” Journal of biomedical informatics, vol. 105, p. 103411, 2020.
[4] K. L. Lanctôt, J. Amatniek, S. Ancoli-Israel, et al., “Neuropsychiatric signs and symptoms of
alzheimer’s disease: New treatment paradigms,” Alzheimer’s & Dementia: Translational Research
&Clinical Interventions, vol. 3, no. 3, pp. 440–449, 2017.
[5] D.Carmo,B.Silva,C.Yasuda,L.Rittner,andR.Lotufo,“Hippocampussegmentationonepilepsy
and alzheimer’s disease studies with multiple convolutional neural networks,” Heliyon, vol. 7,
no. 2, 2021.
[6] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image
segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015:
18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18,
Springer, 2015, pp. 234–241.
[7] M. A. Hossain and M. S. A. Sajib, “Classification of image using convolutional neural network
(cnn),” Global Journal of Computer Science and Technology, vol. 19, no. 2, pp. 13–14, 2019.
[8] K.I.Limited,Understandingconvolutionneural network(cnn) architecture–deep learning, 2023.
[Online]. Available: https://www.ksolves.com/blog/artificial-intelligence/
understanding-convolution-neural-network-architecture.
[9] A.D.NISHAD,Convolutionneuralnetwork,2020.[Online]. Available: https://www.kaggle.
com/discussions/general/171197.
[10] A.Jayasankha, Build your own model with convolutional neural networks, 2020. [Online]. Avail
able: https://medium.com/analytics-vidhya/build-your-own-model-with
convolutional-neural-networks-5ca0dd222c8f.
[11] D.P.KingmaandJ.Ba,“Adam:Amethodforstochasticoptimization,”arXivpreprintarXiv:1412.6980,
2014.
[12] J. Yousefi, “Image binarization using otsu thresholding algorithm,” Ontario, Canada: University
of Guelph, vol. 10, 2011.
[13] J. Lee, J. Oh, S. K. Shah, X. Yuan, and S. J. Tang, “Automatic classification of digestive organs
in wireless capsule endoscopy videos,” in Proceedings of the 2007 ACM symposium on Applied
computing, 2007, pp. 1041–1045.指導教授 洪盟凱(Meng-Kai Hong) 審核日期 2024-7-23 推文 plurk
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