基于物联网和深度转移学习的乳腺癌诊断,由雾计算实现
Abhilash Pati1, Manoranjan Parhi2, Binod Kumar Pattanayak1
1Department of Computer Science and Engineering, Faculty of Engineering and Technology (ITER), Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar 751030, India.
Diagnostics (Basel, Switzerland)
|July 14, 2023
概括
这项研究引入了深度转移学习 (DTL) 模型,用于使用乳腺图像进行自主乳腺癌诊断. 该模型实现了高精度,改善了早期检测和患者的结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 乳腺癌对全球健康构成重大风险,早期诊断对于有效治疗至关重要.
- 医学图像分析和物联网 (IoT) 的技术进步使得更快,更准确的疾病检测成为可能.
- 乳腺癌等慢性疾病的早期和远程诊断越来越依赖于物联网集成.
研究的目的:
- 开发一个自主乳腺癌诊断系统,使用乳房图像的深度转移学习 (DTL).
- 通过先进的人工智能技术,提高乳腺癌检测的准确性和效率.
- 利用DTL和雾计算实现安全,高效和高性能医疗图像分析.
主要方法:
- 使用来自癌症成像档案 (TCIA) 的乳房图像训练了一个DTL模型.
- 结合深度学习 (DL) 技术,如卷积神经网络 (CNN) 与转移学习 (TL) 模型 (ResNet50,InceptionV3,AlexNet,VGG16,VGG19) 以及支持向量机 (SVM) 分类器.
- 利用雾计算来增强数据隐私,安全性和减少服务器负载.
主要成果:
- DTL模型实现了高性能指标:97.99%的准确性,99.51%的精度,98.43%的灵敏度,80.08%的特异性,98.97%的f1-score.
- 与现有方法相比,该系统在大量良性和恶性乳房造影图像数据集上表现出卓越的性能.
- 雾计算集成提高了数据安全性和系统效率.
结论:
- 拟议的DTL模型为自主乳腺癌诊断提供了一种可行和有效的方法.
- 该系统显示了改善早期乳腺癌检测率和患者护理的巨大潜力.
- 整合DTL和雾计算为安全和高效的医疗诊断系统提供了一个有希望的方向.
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