| 摘要: |
| 阿尔茨海默症( Alzheimer’ s Disease,AD) 缺乏有效治疗方法,给患者和社会带来了巨大的危害,为弥补
现有方法的不足,提出一种利用长期视觉特征检测阿尔茨海默症的创新方法。 方法 首先从个体在日常环境中行走
的视频中提取 17 个人体关键点坐标,涵盖手掌、手臂、肩膀、脚踝、膝盖、胯、躯干和头部等关键区域。 这些关键点
数据被组合成一个带有长期视觉特征的数据集,通过这样的数据集输入到序列神经网络进行训练。 结果 实验结果
显示:该方法在阿尔茨海默症检测中表现出色,最终的检测正确率达到 0. 96,F1 值为 0. 93,证明该方法在性能上具
有卓越的表现,进一步强调了长期视觉特征在阿尔茨海默症检测中的重要性。 结论 该方法为阿尔茨海默症的检测
提供了一种有效的途径,弥补了目前治疗方案的空白;同时,实验结果凸显了时序处理网络在该任务中的显著作用;未
来的研究需要更深入地探讨和优化时序处理网络的结构和功能,以进一步提高阿尔茨海默症检测的精确性和可靠性;
这一创新性方法为未来疾病早期诊断和治疗提供了新的方向,为相关领域的研究和应用带来了希望。 |
| 关键词: 深度神经网络 阿尔茨海默症 长期视觉特征 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Alzheimer's Disease Detection Based on Long-term Visual Features |
|
HUANG Meng
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School of Computer Science and Engineering Anhui University of Science and Technology Anhui Huainan 232001 China
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| Abstract: |
| Objective Alzheimer?? s disease AD lacks effective treatments causing significant harm to patients and
society. To address the limitations of existing methods this paper proposed an innovative approach to detect Alzheimer?? s
disease using long-term visual features. Methods First 17 human keypoint coordinates were extracted from videos of
individuals walking in their daily environments covering key regions such as palms arms shoulders ankles knees
hips torsos and heads. These keypoints were combined into a dataset with long-term visual features and the dataset was
fed into a sequential neural network for training. Results The experimental results showed that the proposed method
performed well in Alzheimer?? s disease detection with a final detection accuracy of 0. 96 and an F1 score of 0. 93. This
demonstrated the superior performance of the method and further emphasized the importance of long-term visual features in
Alzheimer?? s disease detection. Conclusion The proposed method provides an effective approach to Alzheimer?? s disease
detection filling the gap in current diagnostic options. Meanwhile the experimental results highlight the significant role of
temporal processing networks in this task. Future studies should explore and optimize the structure and function of
temporal processing networks in greater depth to further improve the accuracy and reliability of Alzheimer?? s disease
detection. This innovative approach offers a new direction for the early diagnosis and treatment of Alzheimer?? s disease and
holds promise for research and applications in related fields. |
| Key words: deep neural networks Alzheimer s disease long-term visual features |
