Breast cancer, the most common cancer in women, is receiving increasing attention. The lack of high-quality medical resources, especially highly skilled doctors, in remote areas makes the diagnosis of breast cancer inefficient and causes great harm to women. The emergence of remote e-health has improved the situation to a certain extent, but its capabilities are still hampered by technical limitations, which manifest in two main aspects. First, due to network bandwidth limitations, it is difficult to guarantee the real-time transmission of breast cancer pathology images between remote areas and cities. Second, the highly skilled breast cancer doctors at large city hospitals are not guaranteed to be available for online diagnosis at all times. To overcome these limitations, this article proposes a deep-learning-empowered breast cancer auxiliary diagnosis scheme for remote e-health supported by 5G technology and beyond (5GB remote e-health). In this scheme, breast pathology images are first received from major hospitals via 5G, and a deep learning model based on the Inception-v3 network is subjected to transfer learning to obtain a diagnostic model. This diagnostic model is then employed on edge servers for auxiliary diagnosis at remote area hospitals. A theoretical analysis and experimental results show that this solution not only overcomes the two problems mentioned above but also improves the diagnostic accuracy for breast cancer in remote areas to 98.19 percent.

中文翻译：

---

用于 5GB 远程电子健康的深度学习赋能的乳腺癌辅助诊断

乳腺癌是女性最常见的癌症，越来越受到关注。偏远地区缺乏优质的医疗资源，尤其是高技能的医生，使得乳腺癌的诊断效率低下，对女性造成极大伤害。远程电子健康的出现在一定程度上改善了这种情况，但其能力仍然受到技术限制的制约，主要表现在两个方面。首先，由于网络带宽限制，难以保证偏远地区和城市之间乳腺癌病理图像的实时传输。其次，不能保证大城市医院的高技能乳腺癌医生随时可以在线诊断。为了克服这些限制，本文针对5G及以上技术支持的远程电子健康提出了一种深度学习赋能的乳腺癌辅助诊断方案（5GB远程电子健康）。该方案首先通过5G从各大医院接收乳腺病理图像，对基于Inception-v3网络的深度学习模型进行迁移学习，得到诊断模型。然后在边缘服务器上采用该诊断模型，用于偏远地区医院的辅助诊断。理论分析和实验结果表明，该方案不仅克服了上述两个问题，而且将偏远地区乳腺癌的诊断准确率提高到98.19%。首先通过5G从各大医院接收乳腺病理图像，基于Inception-v3网络的深度学习模型进行迁移学习，得到诊断模型。然后在边缘服务器上采用该诊断模型，用于偏远地区医院的辅助诊断。理论分析和实验结果表明，该方案不仅克服了上述两个问题，而且将偏远地区乳腺癌的诊断准确率提高到98.19%。首先通过5G从各大医院接收乳腺病理图像，基于Inception-v3网络的深度学习模型进行迁移学习，得到诊断模型。然后在边缘服务器上采用该诊断模型，用于偏远地区医院的辅助诊断。理论分析和实验结果表明，该方案不仅克服了上述两个问题，而且将偏远地区乳腺癌的诊断准确率提高到98.19%。