论文标题
使用转移学习和加权损失的卷积神经网络（CNN）对阿尔茨海默氏病进行分类
Classification of Alzheimer's Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss
论文作者
论文摘要
阿尔茨海默氏病是一种进行性神经退行性疾病，逐渐剥夺患者的认知功能，并可能以死亡结束。随着当今技术的发展，可以通过磁共振成像（MRI）扫描来检测阿尔茨海默氏病。因此，MRI是最常用于诊断和分析阿尔茨海默氏病进展的技术。有了这项技术，可以使用机器学习自动实现对阿尔茨海默氏病的早期诊断的图像识别。尽管机器学习具有许多优势，但目前使用深度学习的应用更广泛地应用，因为它具有更强的学习能力，并且更适合解决图像识别问题。但是，仍然存在一些挑战以实施深度学习，例如需要大型数据集，需要大量计算资源以及需要仔细的参数设置以防止过度拟合或不足。在应对使用深度学习对阿尔茨海默氏病进行分类的挑战时，本研究提出了使用残留网络18层（RESNET-18）体系结构的卷积神经网络（CNN）方法。为了克服对大型且平衡的数据集的需求，使用来自ImageNet的传输学习并加权损耗函数值，以使每个类具有相同的权重。而且，在这项研究中，通过将网络激活函数更改为MISH激活函数以提高准确性，进行了实验。从已经进行的测试结果中，使用转移学习，加权损失和MISH激活函数的模型的准确性为88.3％。该准确性值来自基线模型，该模型仅获得69.1％的精度。
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 %.
