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Classification of Neurotransmitters

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

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Updated: Jul 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

使用CNN的NeuroNet57和殖民地优化来对女性自闭症进行分类.

Adnan Ashraf¹, Qingjie Zhao¹, Waqas Haider Bangyal²

¹School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.

Computers in biology and medicine

|March 8, 2025

概括

这项研究介绍了NeuroNet57,这是一种用于识别女性使用fMRI扫描的自闭症谱系障碍 (ASD) 的深度学习模型. 该模型实现了高精度,提高了自闭症女性的诊断效率.

关键词:

殖民地优化殖民地优化自闭症谱系障碍自闭症谱系障碍深度神经网络是一个神经网络.机器学习机器学习磁共振成像技术磁共振成像技术

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科学领域:

神经科学是一个神经科学.
医疗成像医学成像
人工智能的人工智能

背景情况:

使用医学图像分析识别和分类自闭症正在取得进展.
与男性相比,自闭症女性表现出明显的表型和年龄相关的大脑变异.
有限的女性表型和基因型数据阻碍了准确的诊断和手动评估.

研究的目的:

开发一种先进的深度学习模型,用于准确诊断女性自闭症谱系障碍 (ASD) 的医学诊断.
解决自闭症研究中女性特异性数据的不足.
为了提高诊断自闭症女性的效率.

主要方法:

提出了一个57层卷积神经网络 (CNN) 架构,NeuroNet57,用于从fMRI数据中提取特征.
在脑瘤数据集上预训练NeuroNet57,然后将其应用于自闭症脑成像数据交换 (ABIDE) -I+II数据集,使用T1模式的fMRI扫描.
利用群优化 (ACO) 进行特征子集选择和维度减少,然后使用九种机器学习算法进行分类.

主要成果:

NeuroNet57提取了ABIDE_I的14372 × 4096的特征矩阵,以及ABIDE_II的16168 × 4096的特征矩阵.
基于K-最近邻居 (KNN) 的fineKNN (FKNN) 分类器在ABIDE-I上达到92.21%的准确性,在ABIDE_II.II上达到93.49%的准确性.
拟议的模型在将ASD女性从对照对象分类方面表现出有效性.

结论:

与ACO和FKNN相结合的NeuroNet57模型显示,在女性中准确有效地识别自闭症谱系障碍方面,有显著的前景.
这项研究强调了深度学习在分析神经成像数据以诊断性别特异性自闭症方面的潜力.
用更大,更多样化的数据集进行进一步的研究是有必要的,以验证和完善这些发现.