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Abstract：
Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer\'s disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer\'s prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.
摘要：
深度学习在识别复杂结构方面发挥着重要作用，与传统算法相比，它在训练和分类任务方面的表现优于传统算法。在这项工作中,开发了一种基于云的本地解决方案，用于将阿尔茨海默病（AD）分类为MRI扫描作为输入模态。多分类用于AD品种，分为四个阶段。为了利用预先训练的GoogLeNet模型的功能，采用迁移学习。GoogLeNet模型,它是为图像分类任务预先训练的，针对多类别AD分类的特定目的进行了微调。通过这个过程,达到了98%的更好的准确度。因此,使用GoogLeNet提出的体系结构开发了用于阿尔茨海默病预测的本地云Web应用程序。此应用程序使医生能够远程检查患者是否存在AD。
