论文标题
糖尿病足溃疡通过使用超级分辨率和降噪深度学习技术来监测
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques
论文作者
论文摘要
糖尿病足溃疡（DFUS）对于糖尿病患者来说是严重的并发症。为了实现现有溃疡的早期诊断，预防溃疡和并发症管理，可以通过自我管理来大大改善DFU患者的护理。在本文中，我们研究了两类图像到图像翻译技术（ITITT），它们将支持糖尿病足溃疡的决策和监测：降低降噪和超分辨率。在前一种情况下，我们调查了消除噪声的功能，用于卷积神经网络堆叠的自动编码器（CNN-SAE）。在用高斯噪声引起的RGB图像上测试了CNN-SAE。后一种情况涉及部署四个深度学习超分辨率模型。对于两种情况，所有模型的性能均以执行时间和可感知的质量进行评估。结果表明，应用技术组成了可行的易于实现的替代方法，该替代方法应由设计用于DFU监视的任何系统使用。
Diabetic foot ulcers (DFUs) constitute a serious complication for people with diabetes. The care of DFU patients can be substantially improved through self-management, in order to achieve early-diagnosis, ulcer prevention, and complications management in existing ulcers. In this paper, we investigate two categories of image-to-image translation techniques (ItITT), which will support decision making and monitoring of diabetic foot ulcers: noise reduction and super-resolution. In the former case, we investigated the capabilities on noise removal, for convolutional neural network stacked-autoencoders (CNN-SAE). CNN-SAE was tested on RGB images, induced with Gaussian noise. The latter scenario involves the deployment of four deep learning super-resolution models. The performance of all models, for both scenarios, was evaluated in terms of execution time and perceived quality. Results indicate that applied techniques consist a viable and easy to implement alternative that should be used by any system designed for DFU monitoring.
