Aiadi Oussama, Khaldi Belal
Artifcial Intelligence and Information Technology Laboratory (LINATI), Department of Computer Science and Information Technology, Faculty of Sciences and Technology, University of Kasdi Merbah, Ouargla 3000, Algeria.
Biomed Signal Process Control. 2022 Sep;78:103925. doi: 10.1016/j.bspc.2022.103925. Epub 2022 Jun 21.
With the outbreak of COVID-19 and the increasing number of infections worldwide, there has been a noticeable deficiency in healthcare provided by medical professionals. To cope with this situation, computational methods can be used in different steps of COVID-19 handling. The first step is to accurately and rapidly diagnose infected persons, because the time taken for the diagnosis is among the crucial factors to save human lives. This paper proposes a computationally fast network for the diagnosis of COVID-19 and pulmonary diseases, which can be used in telemedicine. The proposed network is called DLNet because it jointly encodes local binary patterns along with filter outputs of discrete cosine transform (DCT). The first layer in DLNet is the convolution layer in which the input image is convolved using DCT filters. Then, to avoid over-fitting, a binary hashing procedure is performed by fusing responses of different filters into a unique feature map. This map is used to generate block-wise histograms by binding local binary codes of the input image and the map values. We normalize these histograms to improve the robustness of the network against illumination changes. Experiments conducted on a public dataset demonstrate the rapidity and effectiveness of DLNet, where an average accuracy, sensitivity, and specificity of 98.86%, 98.06, and 99.24% have been achieved, respectively. Moreover, the proposed network has shown high tolerance to the missing parts in the medical image, which makes it suitable for the telemedicine scenario.
随着新冠疫情的爆发以及全球感染人数的增加，医疗专业人员提供的医疗服务存在明显不足。为应对这种情况，可在新冠疫情处理的不同环节使用计算方法。第一步是准确快速地诊断感染者，因为诊断所需时间是拯救生命的关键因素之一。本文提出了一种用于诊断新冠和肺部疾病的计算速度快的网络，可用于远程医疗。所提出的网络称为DLNet，因为它将局部二值模式与离散余弦变换（DCT）的滤波器输出联合编码。DLNet的第一层是卷积层，其中使用DCT滤波器对输入图像进行卷积。然后，为避免过拟合，通过将不同滤波器的响应融合到一个独特的特征图中执行二进制哈希过程。该图用于通过绑定输入图像的局部二值码和图值来生成逐块直方图。我们对这些直方图进行归一化以提高网络对光照变化的鲁棒性。在一个公共数据集上进行的实验证明了DLNet的快速性和有效性，其平均准确率、灵敏度和特异性分别达到了98.86%、98.06和99.24%。此外，所提出的网络对医学图像中的缺失部分表现出高耐受性，这使其适用于远程医疗场景。
