A Mobile App for Wound Localization Using Deep Learning

We present an automated wound localizer from 2D wound and ulcer images using a deep neural network as the first step towards building an automatic and complete wound diagnostic system. The wound localizer is developed using the 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 help 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 image dataset, collaborating with AZH Wound and Vascular Center, Milwaukee, Wisconsin. The YOLOv3 model is compared with the SSD model in our developed dataset (AZH wound database, available at https://github.com/uwm-bigdata/wound_localization), showing that YOLOv3 gives an 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, which shows very good performance. Finally, a bigger dataset (BMAZHM Wound Database) is created, and the developed model is retrained with the new dataset to achieve a very high mAP of 97.3%.

Automated summary

An automated wound localizer was developed using deep neural networks to detect wounds and surrounding tissues, achieving high accuracy on different datasets. The YOLOv3 model outperformed the SSD model in terms of mean Average Precision (mAP), demonstrating robustness and reliability across various datasets.