Abstract. Burns are one of the obnoxious injuries subjecting thousands to loss of life and physical defacement each year. Both high income and Third World countries face major evaluation challenges including but not limited to inadequate workforce, poor diagnostic facilities, inefficient diagnosis and high operational cost. As such, there is need to develop an automatic machine learning algorithm to noninvasively identify skin burns. This will operate with little or no human intervention, thereby acting as an affordable substitute to human expertise. We leverage the weights of pretrained deep neural networks for image description and, subsequently, the extracted image features are fed into the support vector machine for classification. To the best of our knowledge, this is the first study that investigates black African skins. Interestingly, the proposed algorithm achieves state-of-the-art classification accuracy on both Caucasian and African datasets.

中文翻译：

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使用白种人和非洲患者的图像对人体皮肤烧伤进行无创评估和分类

摘要。烧伤是每年导致数千人丧生和身体受损的令人讨厌的伤害之一。高收入国家和第三世界国家都面临着重大的评估挑战，包括但不限于劳动力不足、诊断设施差、诊断效率低下和运营成本高。因此，需要开发一种自动机器学习算法来无创识别皮肤烧伤。这将在很少或没有人为干预的情况下运行，从而成为人类专业知识的可负担替代品。我们利用预训练深度神经网络的权重进行图像描述，随后将提取的图像特征输入支持向量机进行分类。据我们所知，这是第一项调查非洲黑皮的研究。有趣的是，