基于3D-ResNet的阿尔兹海默症分类算法研究

An Alzheimer’s disease classification algorithm based on 3D-ResNet

阿尔兹海默症(AD)是一种不可逆的神经退行性大脑疾病,也是老年人群中最常见的痴呆症。人工分类阿尔兹海默症的核磁共振影像(MRI)存在分类延迟和分类耗时等问题。随着人口老龄化的日趋严重,准确而快速地分类出阿尔兹海默症患者具有重要的研究意义。将卷积神经网络(CNN)技术和核磁共振成像技术相结合,设计了一个3D-ResNet算法用于AD分类,在验证集上取得了98.39%的准确性、96.74%的敏感性和99.99%的特异性,在测试集上取得了97.43%的准确性、94.92%的敏感性和99.99%的特异性,每个患者的分类时间是0.23 s。此外,针对AD的发病机制尚不明确的问题,通过类激活映射(CAM)技术来可视化与AD相关的脑部区域。

Alzheimer’s disease(AD)is an irreversible neuro degenerative brain disease and the most common dementia in the elderly.Manual classification of Alzheimer’s magnetic resonance image(MRI)has problems delay classification and time-consuming classification.As the aging population becomes more and more serious,accurately and quickly classify patients with AD has important research significance.This paper combines convolutional neural network(CNN)technology with MRI technology,and designs a 3D-ResNet model for AD classification,which achieves 98.39%accuracy,96.74%sensitivity and 99.99%specificity on the validation set and achieves 97.43%accuracy,94.92%sensitivity and 99.99%specificity on the test set.The classification time of each patient is 0.23 s.In addition,for the problem that the pathogenesis of AD is not yet clear,this paper uses Class Activation Mapping(CAM)technology to visualize the AD-related brain regions.