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Multi-feature representation for burn depth classification via burn images
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2021-06-27 , DOI: 10.1016/j.artmed.2021.102128
Bob Zhang 1 , Jianhang Zhou 1
Affiliation  

Burns are a common and severe problem in public health. Early and timely classification of burn depth is effective for patients to receive targeted treatment, which can save their lives. However, identifying burn depth from burn images requires physicians to have a lot of medical experience. The speed and precision to diagnose the depth of the burn image are not guaranteed due to its high workload and cost for clinicians. Thus, implementing some smart burn depth classification methods is desired at present. In this paper, we propose a computerized method to automatically evaluate the burn depth by using multiple features extracted from burn images. Specifically, color features, texture features and latent features are extracted from burn images, which are then concatenated together and fed to several classifiers, such as random forest to generate the burn level. A standard burn image dataset is evaluated by our proposed method, obtaining an Accuracy of 85.86% and 76.87% by classifying the burn images into two classes and three classes, respectively, outperforming conventional methods in the burn depth identification. The results indicate our approach is effective and has the potential to aid medical experts in identifying different burn depths.



中文翻译:

通过烧伤图像进行烧伤深度分类的多特征表示

烧伤是公共卫生中常见且严重的问题。及早及时对烧伤深度进行分类,有利于患者接受针对性治疗,挽救生命。然而,从烧伤图像中识别烧伤深度需要医生具有丰富的医疗经验。由于临床医生的工作量和成本高,因此无法保证诊断烧伤图像深度的速度和精度。因此,目前需要实现一些智能的烧伤深度分类方法。在本文中,我们提出了一种计算机化方法,通过使用从烧伤图像中提取的多个特征来自动评估烧伤深度。具体来说,从烧伤图像中提取颜色特征、纹理特征和潜在特征,然后将它们连接在一起并馈送到多个分类器,例如随机森林来生成燃烧级别。通过我们提出的方法评估标准烧伤图像数据集,通过将烧伤图像分为两类和三类,分别获得 85.86% 和 76.87% 的准确率,在烧伤深度识别方面优于传统方法。结果表明我们的方法是有效的,并且有可能帮助医学专家识别不同的烧伤深度。

更新日期:2021-07-08
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