Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention.
Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention.
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DOI：
10.1109/ojemb.2021.3092207
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发表时间：
2021
影响因子：
5.8
通讯作者：
Strong D
中科院分区：
文献类型：
--
作者：
Liu Z;Agu E;Pedersen P;Lindsay C;Tulu B;Strong D
目标：慢性伤口影响650万美国人。通过智能手机图像的算法分析进行伤口评估已成为远程评估的可行选择。研究方法：我们基于临床验证的伤口摄影评估工具（PWAT）对伤口进行全面评分，该工具全面评估八个伤口属性的临床重要范围：尺寸、深度、坏死组织类型、坏死组织量、肉芽组织类型、肉芽组织量、边缘、溃疡周围皮肤活力。我们提出了一个DenseNet卷积神经网络（CNN）框架，该框架具有基于补丁的上下文保持注意力，以评估四种伤口类型的8个PWAT属性：糖尿病溃疡，压疮，血管溃疡和手术伤口。结果如下：在对我们的1639张伤口图像数据集的评估中，我们的模型估计了所有8个PWAT子评分，分类准确度和F1评分超过80%。结论：我们的工作是第一个智能系统，自主分级伤口全面的标准的基础上PWAT的标题，减轻了重大负担，手动伤口分级伤口护理护士。
Goal: Chronic wounds affect 6.5 million Americans. Wound assessment via algorithmic analysis of smartphone images has emerged as a viable option for remote assessment. Methods: We comprehensively score wounds based on the clinically-validated Photographic Wound Assessment Tool (PWAT), which comprehensively assesses clinically important ranges of eight wound attributes: Size, Depth, Necrotic Tissue Type, Necrotic Tissue Amount, Granulation Tissue type, Granulation Tissue Amount, Edges, Periulcer Skin Viability. We proposed a DenseNet Convolutional Neural Network (CNN) framework with patch-based context-preserving attention to assess the 8 PWAT attributes of four wound types: diabetic ulcers, pressure ulcers, vascular ulcers and surgical wounds. Results: In an evaluation on our dataset of 1639 wound images, our model estimated all 8 PWAT sub-scores with classification accuracies and F1 scores of over 80%. Conclusions: Our work is the first intelligent system that autonomously grades wounds comprehensively based on criteria in the PWAT rubric, alleviating the significant burden that manual wound grading imposes on wound care nurses.
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