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

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/95753

Title:	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
Authors:	周大豐;Chou, Da-Feng
Contributors:	數學系
Keywords:	深度學習;卷積神經網路;預訓練模型;阿茲海默症
Date:	2024-07-23
Issue Date:	2024-10-09 17:15:01 (UTC+8)
Publisher:	國立中央大學
Abstract:	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。;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.
Appears in Collections:	[Graduate Institute of Mathematics] Electronic Thesis & Dissertation

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