基于使用轻量级神经网络进行肺癌诊断的传感器图像的VOC分类
Chengyuan Zha1, Lei Li1, Fangting Zhu1
1Department of Electronics and Electrical Engineering, Changchun University of Technology, Changchun 130012, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
一个新的轻量级神经网络 (LTNet) 提供了高精度的肺癌检测,使用呼吸挥发性有机化合物 (VOC) 进行点检测 (POCT) 设备. 通过使用较少的参数和比现有模型更快的处理,LTNet实现了卓越的性能.
科学领域:
- 人工智能用于疾病检测和检测.
- 生物医学工程 生物医学工程
- 计算神经科学是一种计算神经科学.
背景情况:
- 卷积神经网络 (CNN) 显示了通过在临床检测 (POCT) 中通过呼出的挥发性有机化合物 (VOC) 来检测肺癌的前景.
- 目前的CNN模型在准确性,计算复杂性和参数大小方面面临挑战,这阻碍了它们在资源有限的POCT设备上部署.
研究的目的:
- 开发一个轻量级的神经网络 (LTNet),以使用POCT.进行高效和准确的肺癌检测.
- 为了实现亚和乙醇的高精度分类,这是肺癌的关键呼吸道标志物.
主要方法:
- 为嵌入式系统设计了一种新的轻量级神经网络 (LTNet).
- 评估了LTNet在混合气体和加利福尼亚大学 (UCI) 数据集上的表现.
- 将LTNet与既有轻量级CNN模型 (如EfficientNet.Net) 进行比较.
主要成果:
- 与六个现有模型相比,LTNet表现出更高的分类准确性 (99.06%和99.14%).
- LTNet实现了显著缩短的培训和推断时间.
- LTNet拥有最小的参数数量 (32K) 和小的训练重量大小 (0.155MB).
结论:
- 由于其效率和准确性,LTNet非常适合资源有限的POCT设备.
- 拟议的LTNet推进了人工智能驱动的呼吸分析领域,用于非侵入性疾病检测.
- 通过可访问的POCT技术,LTNet为改善肺癌查提供了可行的解决方案.
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