COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biocybernetics and Biomedical Engineering Pub Date : 2023-01-01 DOI:10.1016/j.bbe.2022.11.003
Gerosh Shibu George , Pratyush Raj Mishra , Panav Sinha , Manas Ranjan Prusty
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引用次数: 13

Abstract

COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Most of existing methods used for this application involve the usage of pretrained models such as VGG19, ResNet, DenseNet, Xception, and EfficeintNet which were trained on RGB image datasets. X-rays are fundamentally single channel images, hence using RGB trained model is not appropriate since it increases the operations by involving three channels instead of one. A way of using pretrained model for grayscale images is by replicating the one channel image data to three channel which introduces redundancy and another way is by altering the input layer of pretrained model to take in one channel image data, which comprises the weights in the forward layers that were trained on three channel images which weakens the use of pre-trained weights in a transfer learning approach. A novel approach for identification of COVID-19 using CXRs, Contrast Limited Adaptive Histogram Equalization (CLAHE) along with Homomorphic Transformation Filter which is used to process the pixel data in images and extract features from the CXRs is suggested in this paper. These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. Evaluation of the suggested model is done with the help of two publicly available datasets; one to obtain COVID-19 and No-Finding images and the other to obtain Pneumonia CXRs. The dataset comprises 6750 images in total; 2250 images for each class. Results obtained show that the model has achieved 96.56% for multi-class classification and 98.06% accuracy for binary classification using 5-fold stratified cross validation (CV) method. This result is competitive and up to the mark when compared with the performance shown by existing approaches for COVID-19 classification.

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基于同态变换和VGG启发的深度卷积神经网络的胸部x线图像COVID-19检测
新冠肺炎使整个世界陷入停滞。目前的检测方法既费时又昂贵。使用胸部X光片(CXR)可以解决这个问题,然而,手动检查CXR是一个繁琐而困难的过程,需要该领域的专业化。用于该应用程序的大多数现有方法都涉及使用在RGB图像数据集上训练的预训练模型,如VGG19、ResNet、DenseNet、Xception和EfficientNet。X射线基本上是单通道图像,因此使用RGB训练的模型是不合适的,因为它通过涉及三个通道而不是一个通道来增加操作。将预训练的模型用于灰度图像的一种方式是通过将一通道图像数据复制到引入冗余的三通道,而另一种方式则是通过改变预训练模型的输入层以获取一通道的图像数据,其包括在三个通道图像上训练的前向层中的权重,这削弱了在迁移学习方法中预训练权重的使用。本文提出了一种利用CXR、对比度有限自适应直方图均衡(CLAHE)和同胚变换滤波器识别新冠肺炎的新方法,该方法用于处理图像中的像素数据并从CXR中提取特征。然后将这些处理后的图像作为输入提供给受VGG启发的深度卷积神经网络(CNN)模型,该模型以单通道图像数据作为输入(灰度图像),以将CXR分类为三个类别标签,即No-Findings、新冠肺炎和肺炎。在两个公开可用的数据集的帮助下,对所建议的模型进行了评估;一种用于获得新冠肺炎和无结合图像,另一种用于获取肺炎CXR。该数据集总共包括6750幅图像;每个类别2250张图像。结果表明,使用5倍分层交叉验证(CV)方法,该模型对多类分类的准确率为96.56%,对二元分类的准确度为98.06%。与新冠肺炎现有分类方法所显示的性能相比,这一结果具有竞争力,达到了标准。
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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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