Histology and Embryology Section, Qiqihar Medical University, Qiqihar, China.
The Second Affiliated Hospital of Qiqihar Medical University, Qiqihar, China.
Front Public Health. 2022 Jun 30;10:925147. doi: 10.3389/fpubh.2022.925147. eCollection 2022.
Age-related Macular Degeneration (AMD) is a kind of irreversible vision loss or disease caused by retinal pigment epithelial cells and neuroretinal degeneration, which has become the main cause of vision loss and blindness of the elderly over 65 years old in developed countries. The main clinical manifestations are cognitive decline, mental symptoms and behavioral disorders, and the gradual decline of daily living ability. In this paper, a feature extraction method of electroencephalogram (EEG) signal based on multi-spectral image fusion of multi-brain regions is proposed based on artificial neural network (ANN). In this method, the brain is divided into several different brain regions, and the EEG signals of different brain regions are transformed into several multispectral images by combining with the multispectral image transformation method. Using Alzheimer's disease (AD) classification algorithm, the depth residual network model pre-trained in ImageNet was transferred to sMRI data set for fine adjustment, instead of training a brand-new model from scratch. The results show that the proposed method solves the problem of few available medical image samples and shortens the training time of ANN model.
年龄相关性黄斑变性(AMD)是一种由视网膜色素上皮细胞和神经视网膜变性引起的不可逆转的视力丧失或疾病,已成为发达国家 65 岁以上老年人视力丧失和失明的主要原因。主要临床表现为认知能力下降、精神症状和行为障碍以及日常生活能力逐渐下降。本文提出了一种基于人工神经网络(ANN)的多脑区多光谱图像融合的脑电图(EEG)信号特征提取方法。该方法将大脑分为几个不同的脑区,通过与多光谱图像变换方法相结合,将不同脑区的 EEG 信号变换为几个多光谱图像。使用阿尔茨海默病(AD)分类算法,将在 ImageNet 上预训练的深度残差网络模型转移到 sMRI 数据集进行微调,而不是从头开始训练全新的模型。结果表明,该方法解决了医学图像样本数量少和 ANN 模型训练时间长的问题。