The classification of Alzheimer’s disease (AD) using ADNI dataset requires suitable feature segmenting techniques to detect the existing and relevant finer smaller brain region features, together with effective classification model, to eliminate a massive, labor-intensive and time-consuming voxel-based morphometry technique. Here, in this paper, a deep learning-based segmenting method using SegNet to detect AD pertinent brain parts features from structural magnetic resonance imaging (sMRI) and subsequently classifying accurately AD and dementia condition using ResNet-101 is presented. A deep learning-based image segmenting approach is experimented in detecting the delicate features of brain morphological changes due to AD that benefits classification performance for cognitive normal, mild cognitive impairment and AD, and thus provides an easy automatic diagnosis of Alzheimer’s diseases. For classification, ResNet-101 is trained applying features extracted from SegNet with ADNI dataset. This paper demonstrated particularly to attain top-level automated classification. The seven morphological features like grey matter, white matter, cortex surface, gyri and sulci contour, cortex thickness, hippocampus and cerebrospinal fluid space extracted from 240 sMRI with SegNet are used to train ResNet for classification, and this classifier achieved a sensitivity of 96% and an accuracy of 95% over 240 ADNI sMRI other than used for training.

使用ADNI数据集对阿尔茨海默氏病（AD）进行分类需要合适的特征分割技术，以检测现有的和相关的更精细的较小的大脑区域特征，以及有效的分类模型，以消除基于体素的庞大，费力且费时的庞大形态技术。在此，本文提出了一种基于深度学习的分割方法，该方法使用SegNet从结构磁共振成像（sMRI）中检测AD相关的脑部特征，然后使用ResNet-101准确地对AD和痴呆状况进行分类。实验中使用了一种基于深度学习的图像分割方法来检测由于AD导致的大脑形态变化的微妙特征，从而有益于认知正常，轻度认知障碍和AD的分类性能，从而可以轻松自动诊断阿尔茨海默氏病。为了进行分类，对ResNet-101进行了训练，使用从SegNet中提取的具有ADNI数据集的特征。本文特别展示了获得顶级自动化分类的方法。使用SegNet从240 sMRI中提取的七个形态特征（如灰质，白质，皮质表面，回旋和沟轮廓，皮质厚度，海马和脑脊髓液空间）用于训练ResNet进行分类，该分类器的灵敏度达到96％除用于训练外，在240个ADNI sMRI上的准确性为95％。