基于浏览器的多种癌症分类框架使用深度可分离的卷积用于精确诊断.
Divine Sebukpor1, Ikenna Odezuligbo2, Maimuna Nagey3
1Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India.
Diagnostics (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了一种基于浏览器的AI,用于多种癌症的分类,达到99.85%的准确性,不需要强大的硬件或服务器. 这种保护隐私的深度学习框架可以在全球范围内进行实时癌症诊断.
科学领域:
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
- 计算病理学计算病理学
背景情况:
- 精确的癌症检测至关重要,但深度学习的硬件和隐私要求阻碍了这一点.
- 现有的AI解决方案往往需要高性能计算和集中式数据存储.
- 数据隐私问题限制了AI在敏感医疗应用中的应用.
研究的目的:
- 开发基于浏览器的客户端深度学习框架,用于多种癌症的分类.
- 在没有专门的基础设施的情况下,实现实时,保护隐私的癌症诊断.
- 在资源有限的环境中展示高精度AI部署的可行性.
主要方法:
- 在超过13万张多种癌症图像上使用深度可分离卷积微调了Xception架构.
- 实现了一个基于浏览器的框架,使用TensorFlow.js进行客户端推理.
- 与VGG16,ResNet50,EfficientNet-B0和视觉变压器模型进行基准性能测试.
主要成果:
- 实现了 99.85% 的 Top-1 精度和 100% 的 Top-5 精度,超过了所有比较器.
- 证明了适合标准Web浏览器的轻量级计算要求.
- 格拉德-CAM可视化证实预测是基于相关的组织病理特征,确保可解释性.
结论:
- 介绍了第一个完全可以在浏览器中部署的,保护隐私的深度学习框架,用于多种癌症诊断.
- 高精度的医疗AI可以在没有重要的基础设施开销或专业硬件的情况下实现.
- 建立了一个实用的途径,以公平和成本效益的全球部署AI在癌症诊断.
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