A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module.

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yiwen Liu, Wenyu Xing, Mingbo Zhao, Mingquan Lin
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引用次数: 0

Abstract

During the past three years, the coronavirus disease 2019 (COVID-19) has swept the world. The rapid and accurate recognition of covid-19 pneumonia are ,therefore, of great importance. To handle this problem, we propose a new pipeline of deep learning framework for diagnosing COVID-19 pneumonia via chest X-ray images from normal, COVID-19, and other pneumonia patients. In detail, the self-trained YOLO-v4 network was first used to locate and segment the thoracic region, and the output images were scaled to the same size. Subsequently, the pre-trained convolutional neural network was adopted to extract the features of X-ray images from 13 convolutional layers, which were fused with the original image to form a 14-dimensional image matrix. It was then put into three parallel pyramid multi-layer perceptron (MLP)-Mixer modules for comprehensive feature extraction through spatial fusion and channel fusion based on different scales so as to grasp more extensive feature correlation. Finally, by combining all image features from the 14-channel output, the classification task was achieved using two fully connected layers as well as Softmax classifier for classification. Extensive simulations based on a total of 4099 chest X-ray images were conducted to verify the effectiveness of the proposed method. Experimental results indicated that our proposed method can achieve the best performance in almost all cases, which is good for auxiliary diagnosis of COVID-19 and has great clinical application potential.

Abstract Image

Abstract Image

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一种新的诊断新冠肺炎肺炎的分类方法,基于胸部X射线图像的联合CNN特征和平行金字塔MLP-mixer模块。
在过去三年中,2019冠状病毒病(新冠肺炎)席卷全球。因此,快速准确地识别新冠肺炎肺炎具有重要意义。为了解决这个问题,我们提出了一种新的深度学习框架,用于通过正常、新冠肺炎和其他肺炎患者的胸部X光图像诊断COVID-19]肺炎。详细地说,首先使用自训练的YOLO-v4网络来定位和分割胸部区域,并将输出图像缩放到相同的大小。随后,采用预先训练的卷积神经网络从13个卷积层中提取X射线图像的特征,并将其与原始图像融合,形成14维图像矩阵。然后将其放入三个平行金字塔多层感知器(MLP)-混合器模块中,通过基于不同尺度的空间融合和通道融合进行综合特征提取,以掌握更广泛的特征相关性。最后,通过组合14通道输出的所有图像特征,使用两个完全连接的层以及Softmax分类器进行分类,实现了分类任务。基于总共4099张胸部X射线图像进行了广泛的模拟,以验证所提出方法的有效性。实验结果表明,我们提出的方法几乎在所有病例中都能达到最佳性能,有利于新冠肺炎的辅助诊断,具有很大的临床应用潜力。
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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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