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首页> 外文期刊>Journal of burn care & research: official publication of the American Burn Association >Time-Independent Prediction of Burn Depth Using Deep Convolutional Neural Networks
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Time-Independent Prediction of Burn Depth Using Deep Convolutional Neural Networks

机译：使用深卷积神经网络的燃烧深度独立于烧伤深度预测

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摘要

We present in this paper the application of deep convolutional neural networks (CNNs), which is a state-of-the-art artificial intelligence (AI) approach in machine learning, for automated time-independent prediction of burn depth. Color images of four types of burn depth injured in first few days, including normal skin and background, acquired by a TiVi camera were trained and tested with four pretrained deep CNNs: VGG-16, GoogleNet, ResNet-50, and ResNet-101. In the end, the best 10-fold cross-validation results obtained from ResNet-101 with an average, minimum, and maximum accuracy are 81.66, 72.06, and 88.06%, respectively; and the average accuracy, sensitivity, and specificity for the four different types of burn depth are 90.54, 74.35, and 94.25%, respectively. The accuracy was compared with the clinical diagnosis obtained after the wound had healed. Hence, application of AI is very promising for prediction of burn depth and, therefore, can be a useful tool to help in guiding clinical decision and initial treatment of burn wounds.
机译：我们在本文中展示了深度卷积神经网络（CNNS）的应用，这是一种最先进的人工智能（AI）方法，用于机器学习，用于自动独立于烧伤深度的预测。在前几天内受伤的四种类型的烧伤深度的彩色图像，包括Tivi摄像机获得的正常皮肤和背景，并使用四个佩戴的深层CNNS：VGG-16，Googlenet，Reset-50和Reset-101进行了测试。最后，从Reset-101获得的最佳10倍交叉验证结果，平均，最小和最大精度分别为81.66,72.06和88.06％;四种不同类型的烧伤深度的平均精度，灵敏度和特异性分别为90.54,74.35和94.25％。将准确性与伤口愈合后获得的临床诊断进行比较。因此，AI的应用对于预测烧伤深度是非常有前途的，因此，可以是有助于引导临床决策和初始治疗烧伤伤口的有用工具。

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