论文标题
使用深度学习的移动应用程序进行伤口本地化
A Mobile App for Wound Localization using Deep Learning
论文作者
论文摘要
我们通过使用深神经网络向2D伤口图像和溃疡图像提供了自动伤口定位器，作为构建自动化和完整伤口诊断系统的第一步。伤口定位器是通过使用Yolov3模型开发的，然后将其变成iOS移动应用程序。发达的定位器可以检测伤口及其周围的组织，并将局部伤员区域从图像中分离出来，这对于将来的处理非常有帮助，例如由于从伤口图像中去除不必要的区域而导致的伤口分割和分类。对于通过视频处理的移动应用程序开发，已使用了名为Tiny-Yolov3的Yolov3的更轻版本。该模型与威斯康星州密尔沃基的AZH伤口和血管中心合作，在我们自己的图像数据集上进行了训练和测试。将Yolov3模型与SSD模型进行了比较，表明Yolov3的地图值为93.9％，这比SSD模型（86.4％）好得多。这些模型的鲁棒性和可靠性还在名为Medetec的公开数据集中进行了测试，并且表现出非常好的性能。
We present an automated wound localizer from 2D wound and ulcer images by using deep neural network, as the first step towards building an automated and complete wound diagnostic system. The wound localizer has been developed by using YOLOv3 model, which is then turned into an iOS mobile application. The developed localizer can detect the wound and its surrounding tissues and isolate the localized wounded region from images, which would be very helpful for future processing such as wound segmentation and classification due to the removal of unnecessary regions from wound images. For Mobile App development with video processing, a lighter version of YOLOv3 named tiny-YOLOv3 has been used. The model is trained and tested on our own image dataset in collaboration with AZH Wound and Vascular Center, Milwaukee, Wisconsin. The YOLOv3 model is compared with SSD model, showing that YOLOv3 gives a mAP value of 93.9%, which is much better than the SSD model (86.4%). The robustness and reliability of these models are also tested on a publicly available dataset named Medetec and shows a very good performance as well.
