Aim and Objective: Fast and accurate diagnosis of Alzheimer's disease is very important for the care and further treatment of patients. Along with the development of deep learning, impressive progress has also been made in the automatic diagnosis of AD. Most existing studies on automatic diagnosis are concerned with a single base network, whose accuracy for disease diagnosis still needs to be improved. This study was undertaken to propose a method to improve the accuracy of the automatic diagnosis of AD.

Materials and Methods: MRI image data from the Alzheimer’s Disease Neuroimaging Initiative were used to train a deep learning model to achieve a computer-aided diagnosis of Alzheimer's disease. The data consisted of 138 with AD, 280 with mild cognitive impairment, and 138 normal controls. Here, a new deeply-fused net is proposed, which combines several deep convolutional neural networks, thereby avoiding the error of a single base network and increasing the classification accuracy and generalization capacity.

Results: Experiments show that when differentiating between patients with AD, mild cognitive impairment, and normal controls on a subset of the ADNI database without data leakage, the new architecture improves the accuracy by about 4 percentage points as compared to a single standard based network.

Conclusion: This new approach exhibits better performance, but there is still much to be done before its clinical application. In the future, greater research effort will be devoted to improving the performance of the deeply-fused net.

目的与目的：快速准确地诊断阿尔茨海默病对于患者的护理和进一步治疗非常重要。随着深度学习的发展，AD的自动诊断也取得了令人瞩目的进展。现有的自动诊断研究大多涉及单一的基础网络，其疾病诊断的准确性仍有待提高。本研究旨在提出一种提高 AD 自动诊断准确性的方法。

材料和方法：来自阿尔茨海默病神经成像计划的 MRI 图像数据用于训练深度学习模型，以实现阿尔茨海默病的计算机辅助诊断。数据包括 138 名 AD、280 名轻度认知障碍和 138 名正常对照。在这里，提出了一种新的深度融合网络，它结合了多个深度卷积神经网络，从而避免了单一基础网络的错误，提高了分类精度和泛化能力。

结果：实验表明，在没有数据泄漏的情况下，在 ADNI 数据库的一个子集上区分 AD、轻度认知障碍和正常对照患者时，与基于单一标准的网络相比，新架构的准确度提高了约 4 个百分点。

结论：这种新方法表现出更好的性能，但在其临床应用之前还有很多工作要做。未来，更多的研究工作将致力于提高深度融合网络的性能。