Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, Kleve, Germany.
Sci Rep. 2024 Nov 22;14(1):28954. doi: 10.1038/s41598-024-80013-0.
Viruses are submicroscopic agents that can infect other lifeforms and use their hosts' cells to replicate themselves. Despite having simplistic genetic structures among all living beings, viruses are highly adaptable, resilient, and capable of causing severe complications in their hosts' bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses pose the biggest biological threat both animal and plant species face. It is often challenging to promptly detect a virus in a host and accurately determine its type using manual examination techniques. However, computer-based automatic diagnosis methods, especially the ones using Transmission Electron Microscopy (TEM) images, have proven effective in instant virus identification. Using TEM images collected from a recent dataset, this article proposes a deep learning-based classification model to identify the virus type within those images. The methodology of this study includes two coherent image processing techniques to reduce the noise present in raw microscopy images and a functional Convolutional Neural Network (CNN) model for classification. Experimental results show that it can differentiate among 14 types of viruses with a maximum of 97.44% classification accuracy and F-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable virus identification scheme subsidiary to the thorough diagnostic procedures.
病毒是一种亚微观的病原体，能够感染其他生命形式，并利用宿主细胞进行自我复制。尽管病毒在所有生物中的遗传结构都很简单，但它们具有高度的适应性、弹性，并且能够在宿主体内引起严重的并发症。由于病毒具有多种传播途径、高传染性和高致死率，因此它们对动植物物种构成了最大的生物威胁。使用人工检查技术，通常很难及时在宿主中检测到病毒并准确确定其类型。然而，基于计算机的自动诊断方法，特别是使用透射电子显微镜 (TEM) 图像的方法，已被证明在即时病毒识别方面非常有效。本文使用最近数据集采集的 TEM 图像，提出了一种基于深度学习的分类模型，用于识别这些图像中的病毒类型。本研究的方法包括两种相干的图像处理技术，用于减少原始显微镜图像中的噪声，以及用于分类的功能卷积神经网络 (CNN) 模型。实验结果表明，它可以区分 14 种病毒，最高分类准确率和 F 分数达到 97.44%，这证明了所提出方法的有效性和可靠性。实施该方案将为全面诊断程序提供快速可靠的病毒识别方案。
