Department of Nursing, Sakarya University, Sakarya, Turkey.
Department of Nursing Van Yuzuncu Yil University, Turkey.
Burns. 2024 May;50(4):966-979. doi: 10.1016/j.burns.2024.01.007. Epub 2024 Jan 15.
This study was conducted to determine the segmentation, classification, object detection, and accuracy of skin burn images using artificial intelligence and a mobile application. With this study, individuals were able to determine the degree of burns and see how to intervene through the mobile application.
This research was conducted between 26.10.2021-01.09.2023. In this study, the dataset was handled in two stages. In the first stage, the open-access dataset was taken from https://universe.roboflow.com/, and the burn images dataset was created. In the second stage, in order to determine the accuracy of the developed system and artificial intelligence model, the patients admitted to the hospital were identified with our own design Burn Wound Detection Android application.
In our study, YOLO V7 architecture was used for segmentation, classification, and object detection. There are 21018 data in this study, and 80% of them are used as training data, and 20% of them are used as test data. The YOLO V7 model achieved a success rate of 75.12% on the test data. The Burn Wound Detection Android mobile application that we developed in the study was used to accurately detect images of individuals.
In this study, skin burn images were segmented, classified, object detected, and a mobile application was developed using artificial intelligence. First aid is crucial in burn cases, and it is an important development for public health that people living in the periphery can quickly determine the degree of burn through the mobile application and provide first aid according to the instructions of the mobile application.
本研究旨在利用人工智能和移动应用程序确定皮肤烧伤图像的分割、分类、目标检测和准确性。通过这项研究，个人能够通过移动应用程序确定烧伤程度并了解如何进行干预。
本研究于 2021 年 10 月 26 日至 2023 年 1 月 9 日进行。在这项研究中，数据集分两个阶段处理。在第一阶段，从 https://universe.roboflow.com/ 获取公开访问数据集，并创建烧伤图像数据集。在第二阶段，为了确定开发系统和人工智能模型的准确性，我们使用自己设计的 Burn Wound Detection Android 应用程序识别住院患者。
在我们的研究中，使用了 YOLO V7 架构进行分割、分类和目标检测。本研究共有 21018 个数据，其中 80%用于训练数据，20%用于测试数据。YOLO V7 模型在测试数据上的成功率为 75.12%。我们在研究中开发的 Burn Wound Detection Android 移动应用程序可用于准确检测个人图像。
在这项研究中，使用人工智能对皮肤烧伤图像进行了分割、分类、目标检测，并开发了一个移动应用程序。在烧伤情况下，急救至关重要，对于生活在偏远地区的人们来说，通过移动应用程序快速确定烧伤程度并根据移动应用程序的指示进行急救是公共卫生的重要发展。
