[论文解读] Classification of Alzheimer's Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss

Muhammad Wildan Oktavian, Novanto Yudistira+1 more|arXiv (Cornell University)|Jul 4, 2022

Public Health and Nutrition被引用 22

一句话总结

该论文使用带有 ImageNet 迁移学习的 ResNet-18 CNN，带加权损失和 Mish 激活，在 ADNI MRI 数据上将 AD、MCI 和 CN 分类，达到 88.3% 的准确率（相比基线 69.1%）。

ABSTRACT

Alzheimer's disease is a progressive neurodegenerative disorder that gradually deprives the patient of cognitive function and can end in death. With the advancement of technology today, it is possible to detect Alzheimer's disease through Magnetic Resonance Imaging (MRI) scans. So that MRI is the technique most often used for the diagnosis and analysis of the progress of Alzheimer's disease. With this technology, image recognition in the early diagnosis of Alzheimer's disease can be achieved automatically using machine learning. Although machine learning has many advantages, currently the use of deep learning is more widely applied because it has stronger learning capabilities and is more suitable for solving image recognition problems. However, there are still several challenges that must be faced to implement deep learning, such as the need for large datasets, requiring large computing resources, and requiring careful parameter setting to prevent overfitting or underfitting. In responding to the challenge of classifying Alzheimer's disease using deep learning, this study propose the Convolutional Neural Network (CNN) method with the Residual Network 18 Layer (ResNet-18) architecture. To overcome the need for a large and balanced dataset, transfer learning from ImageNet is used and weighting the loss function values so that each class has the same weight. And also in this study conducted an experiment by changing the network activation function to a mish activation function to increase accuracy. From the results of the tests that have been carried out, the accuracy of the model is 88.3 % using transfer learning, weighted loss and the mish activation function. This accuracy value increases from the baseline model which only gets an accuracy of 69.1 %.

研究动机与目标

• 使用基于 MRI 的图像分类实现自动早期检测阿尔茨海默病的动机。
• 评估有限数据和类别不平衡时基于 CNN 的架构在阿尔茨海默分类上的表现。
• 研究迁移学习、类别加权和激活函数对模型性能的影响。
• 提出一种缓解数据不平衡并利用预训练来提升准确性的方法。

提出的方法

• 采用残差网络（ResNet）架构，主要是 ResNet-18。
• 应用 ImageNet 的迁移学习以弥补领域特定数据有限的问题。
• 实现加权交叉熵损失，以应对 AD、MCI 和 CN 之间的类别不平衡。
• 在目标层用 Mish 激活替代 ReLU 以评估性能提升。
• 通过去颅标准化和将 3D 扫描转换为 2D 切片以供 CNN 输入进行 MRI 数据预处理。

实验结果

研究问题

• RQ1 来自大规模数据集的迁移学习是否能提高在 ADNI 数据上的阿尔茨海默病 MRI 分类性能？
• RQ2 对损失函数进行加权以解决不平衡是否提升对少数 AD 类的检测以及整体多类准确性？
• RQ3 在此任务中使用 Mish 激活相对于 ReLU 对模型准确性和类级指标有何影响？

主要发现

• 在测试数据上，使用加权损失和 Mish 激活的迁移学习实现了 88.3% 的多类准确率。
• 不采用迁移学习或加权的基线仅实现 69.1% 的准确率，表明所提方法带来显著提升。
• 加权损失提高了 AD 和 CN 类的精确度，并在若干类的 F1 分数上有所提升。
• 迁移学习显著提高了准确性、精确度、召回率和 F1 分数。
• 在结合迁移学习和加权损失时，Mish 激活未进一步提升准确性，尽管影响了一些类级指标。