基于邻域提取的三维卷积神经网络的高光谱图像皮肤伤口分类。

IF 1.3 4区 医学 Q4 ENGINEERING, BIOMEDICAL
Mücahit Cihan, Murat Ceylan
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引用次数: 1

摘要

目的:高光谱成像是一种新兴的成像方式,开始受到医学研究的关注,在临床应用中具有重要的潜力。如今,光谱成像模式,如多光谱和高光谱已经证明了他们提供重要信息的能力,可以帮助更好地表征伤口。损伤组织中的氧变化与正常组织不同。这导致了光谱特性的不同。本研究采用邻域提取三维卷积神经网络方法对皮肤创伤进行分类。方法:详细介绍了高光谱成像的方法,以获得有关受伤和正常组织的最有用的信息。将损伤组织与正常组织的高光谱特征在高光谱图像上进行比较,发现两者存在相对差异。通过利用这些差异,生成了考虑相邻像素的长方体,并使用长方体训练了一个独特设计的三维卷积神经网络模型来提取空间和光谱信息。结果:在不同的长方体空间尺寸和训练/测试率下,评价了该方法的有效性。当训练/测试率为0.9/0.1,长方体空间维数为17时,测试结果为99.69%。结果表明,该方法优于二维卷积神经网络方法,在训练数据较少的情况下也能达到较高的准确率。采用邻域提取三维卷积神经网络方法对损伤区域进行分类,结果表明该方法对损伤区域分类效果良好。此外,分析了邻域提取三维卷积神经网络方法的分类性能和计算时间,并与现有的二维卷积神经网络进行了比较。结论:高光谱成像结合邻域提取三维卷积神经网络作为临床诊断工具,对损伤组织和正常组织的分类效果显著。肤色对所提出的方法的成功没有任何影响。因为不同肤色只有光谱特征的反射率值不同。在不同族群中,受伤组织的光谱特征与正常组织的光谱特征具有相似的光谱特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hyperspectral imaging-based cutaneous wound classification using neighbourhood extraction 3D convolutional neural network.

Objectives: Hyperspectral imaging is an emerging imaging modality that beginning to gain attention for medical research and has an important potential in clinical applications. Nowadays, spectral imaging modalities such as multispectral and hyperspectral have proven their ability to provide important information that can help to better characterize the wound. Oxygenation changes in the wounded tissue differ from normal tissue. This causes the spectral characteristics to be different. In this study, it is classified cutaneous wounds with neighbourhood extraction 3-dimensional convolutional neural network method.

Methods: The methodology of hyperspectral imaging performed to obtain the most useful information about the wounded and normal tissue is explained in detail. When the hyperspectral signatures of wounded and normal tissues are compared on the hyperspectral image, it is revealed that there is a relative difference between them. By taking advantage of these differences, cuboids that also consider neighbouring pixels are generated, and a uniquely designed 3-dimensional convolutional neural network model is trained with the cuboids to extract both spatial and spectral information.

Results: The effectiveness of the proposed method was evaluated for different cuboid spatial dimensions and training/testing rates. The best result with 99.69% was achieved when the training/testing rate was 0.9/0.1 and the cuboid spatial dimension was 17. It is observed that the proposed method outperforms the 2-dimensional convolutional neural network method and achieves high accuracy even with much less training data. The obtained results using the neighbourhood extraction 3-dimensional convolutional neural network method show that the proposed method highly classifies the wounded area. In addition, the classification performance and the2computation time of the neighbourhood extraction 3-dimensional convolutional neural network methodology were analyzed and compared with existing 2-dimensional convolutional neural network.

Conclusions: As a clinical diagnostic tool, hyperspectral imaging, with neighbourhood extraction 3-dimensional convolutional neural network, has yielded remarkable results for the classification of wounded and normal tissues. Skin color does not play any role in the success of the proposed method. Since only the reflectance values of the spectral signatures are different for various skin colors. For different ethnic groups, The spectral signatures of wounded tissue and the spectral signatures of normal tissue show similar spectral characteristics among themselves.

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来源期刊
CiteScore
3.50
自引率
5.90%
发文量
58
审稿时长
2-3 weeks
期刊介绍: Biomedical Engineering / Biomedizinische Technik (BMT) is a high-quality forum for the exchange of knowledge in the fields of biomedical engineering, medical information technology and biotechnology/bioengineering. As an established journal with a tradition of more than 60 years, BMT addresses engineers, natural scientists, and clinicians working in research, industry, or clinical practice.
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