检测多动症的机器学习方法

Varun V Khasnis, Varun K, Prasanna Kumar S Shivaraddi, Prabhu M, Poojari Pavan Kumar
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引用次数: 0

摘要

本报告探讨了深度学习卷积神经网络(CNN)在分析脑磁共振成像扫描中的应用,以协助诊断注意力缺陷多动障碍(ADHD)综合征。多动症是一种影响全球数百万人的神经发育障碍,其特点是注意力困难、多动和冲动。传统的诊断方法依赖于主观评估和行为观察,导致诊断不准确和治疗延误。利用深度学习 CNN 的强大功能进行核磁共振成像分析,有望实现更客观、更高效的诊断,促进及时干预和个性化治疗策略的制定
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Approaches for ADHD Detection
This report explores the application of deep learning convolutional neural networks (CNNs) for analyzing brain MRI scans to assist in the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) syndrome. ADHD is a neuro developmental disorder that affects millions of individuals worldwide, characterized by difficulties in attention, hyperactivity, and impulsivity. Traditional methods of diagnosis rely on subjective assessments and behavioral observations, leading to inaccuracies and delays in treatment. Leveraging the power of deep learning CNNs for MRI analysis offers the potential for more objective and efficient diagnosis, facilitating timely intervention and personalized treatment strategies
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