乳腺癌在低功耗物联网设备上的高效分类:对基因进化U-Net的研究
Mohit Agarwal1, Amit Kumar Dwivedi1, Dibyanarayan Hazra1
1Bennett University, TechZone 2, Greater Noida, 201310, Uttar Pradesh, India.
Computers in biology and medicine
|November 5, 2024
概括
这项研究使用遗传算法压缩了U-Net模型,在物联网设备上将乳腺癌分类的存储量减少了96.12%. 压缩模型显示了最小的1.33%的精度下降,并改善了推断时间.
科学领域:
- 医学成像医学成像
- 计算机科学 计算机科学
- 人工智能的人工智能是人工智能.
背景情况:
- 乳腺癌是一种普遍的疾病,需要早期检测才能有效治疗.
- 自动数据分类有助于及时诊断,但需要大量的计算资源,限制了物联网 (IoT) 设备的部署.
- 像U-Net这样的现有深度学习模型是资源密集型的,阻碍了它们在资源有限的环境中应用.
研究的目的:
- 压缩U-Net模型,以便在物联网设备上有效部署.
- 评估基于遗传算法的压缩技术对医学图像分类的有效性.
- 为了证明压缩方法在不同数据集和模型架构中的适用性.
主要方法:
- 使用遗传算法压缩了U-Net模型,以减少其计算和存储足迹.
- 拟议的压缩方法使用基准乳腺癌数据集进行了评估.
- 模型的有效性在CamVid和土豆叶病数据集上得到进一步验证,并应用于缩小MiniSegNet和FCN 32模型.
主要成果:
- 压缩的U-Net模型实现了96.12%的乳腺癌分类储存量的减少.
- 推理时间在模型压缩后增长了1.97倍.
- 压缩后观察到精度略有下降 (1.33%),表明保留了分类性能.
结论:
- 基于遗传算法的压缩是部署U-Net模型在资源有限的物联网设备上用于乳腺癌检测的有效策略.
- 压缩技术在模型尺寸缩小和最小精度损失之间提供了显著的权衡.
- 该方法表明了多功能性,证明了它对各种数据集和超出U-Net的模型架构有效.
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