论文标题
使用新通道的CNN在胸部X射线图像中检测COVID-19
COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN
论文作者
论文摘要
Covid-19是一种高度传染性的呼吸道感染，影响了世界上大量人群，并继续造成毁灭性后果。必须尽早检测到Covid-19以限制感染的跨度。在这项工作中，提出了一种基于深卷积神经网络（CNN）和通道增强的新分类技术CB-STM-RENET，以筛选胸部X射线中的Covid-19。在这方面，为了了解Covid-19特定的射线照相模式，开发了基于拆分转换 - 合并（STM）的新卷积块。这个新的块系统地将每个分支机构的区域和基于边缘的操作纳入了各个级别的各种特征集，尤其是与区域同质性，质地变化和受感染区域边界相关的特征。提出的CNN体系结构的学习和歧视能力通过利用渠道的增强想法来增强，该渠道的提升想法将辅助通道与原始渠道连接在一起。辅助通道是使用转移学习从预训练的CNN产生的。在三个不同的胸部X射线数据集（即Cov-Healthy-6K，CoV-Noncov-10k和Cov-Noncov-15K）上评估了提出的CB-STM-RENET的有效性。拟议的CB-STM-renet与现有技术的性能比较表现出高性能在区分COVID-19的胸部感染与健康以及其他类型的胸部感染之间。 CB-STM-Renet在这三个数据集上提供了最高的性能；特别是在严格的COV-NONCOV-15K数据集中。拟议技术的良好检测率（97％）和高精度（93％）表明，可以适应Covid-19受感染患者的诊断。该测试代码可在https://github.com/prlab21/covid-19-detection-system-system-using-chest-x-ray-Images上获得。
COVID-19 is a highly contagious respiratory infection that has affected a large population across the world and continues with its devastating consequences. It is imperative to detect COVID-19 at the earliest to limit the span of infection. In this work, a new classification technique CB-STM-RENet based on deep Convolutional Neural Network (CNN) and Channel Boosting is proposed for the screening of COVID-19 in chest X-Rays. In this connection, to learn the COVID-19 specific radiographic patterns, a new convolution block based on split-transform-merge (STM) is developed. This new block systematically incorporates region and edge-based operations at each branch to capture the diverse set of features at various levels, especially those related to region homogeneity, textural variations, and boundaries of the infected region. The learning and discrimination capability of the proposed CNN architecture is enhanced by exploiting the Channel Boosting idea that concatenates the auxiliary channels along with the original channels. The auxiliary channels are generated from the pre-trained CNNs using Transfer Learning. The effectiveness of the proposed technique CB-STM-RENet is evaluated on three different datasets of chest X-Rays namely CoV-Healthy-6k, CoV-NonCoV-10k, and CoV-NonCoV-15k. The performance comparison of the proposed CB-STM-RENet with the existing techniques exhibits high performance both in discriminating COVID-19 chest infections from Healthy, as well as, other types of chest infections. CB-STM-RENet provides the highest performance on all these three datasets; especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), and high precision (93%) of the proposed technique suggest that it can be adapted for the diagnosis of COVID-19 infected patients. The test code is available at https://github.com/PRLAB21/COVID-19-Detection-System-using-Chest-X-Ray-Images.
