Dey Subhrajit, Bhattacharya Rajdeep, Malakar Samir, Schwenker Friedhelm, Sarkar Ram
Department of Electrical Engineering, Jadavpur University, Kolkata, India.
Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
Expert Syst Appl. 2022 Nov 15;206:117812. doi: 10.1016/j.eswa.2022.117812. Epub 2022 Jun 16.
The rapid outbreak of COVID-19 has affected the lives and livelihoods of a large part of the society. Hence, to confine the rapid spread of this virus, early detection of COVID-19 is extremely important. One of the most common ways of detecting COVID-19 is by using chest X-ray images. In the literature, it is found that most of the research activities applied convolutional neural network (CNN) models where the features generated by the last convolutional layer were directly passed to the classification models. In this paper, convolutional long short-term memory (ConvLSTM) layer is used in order to encode the spatial dependency among the feature maps obtained from the last convolutional layer of the CNN and to improve the image representational capability of the model. Additionally, the squeeze-and-excitation (SE) block, a spatial attention mechanism, is used to allocate weights to important local features. These two mechanisms are employed on three popular CNN models - VGG19, InceptionV3, and MobileNet to improve their classification strength. Finally, the Sugeno fuzzy integral based ensemble method is used on these classifiers' outputs to enhance the detection accuracy further. For experiments, three chest X-ray datasets, which are very prevalent for COVID-19 detection, are considered. For all the three datasets, it is found that the results obtained by the proposed method are comparable to state-of-the-art methods. The code, along with the pre-trained models, can be found at https://github.com/colabpro123/CovidConvLSTM.
新型冠状病毒肺炎（COVID-19）的迅速爆发影响了社会很大一部分人的生活和生计。因此，为了限制这种病毒的快速传播，早期检测COVID-19极为重要。检测COVID-19最常见的方法之一是使用胸部X光图像。在文献中发现，大多数研究活动应用了卷积神经网络（CNN）模型，其中最后一个卷积层生成的特征被直接传递到分类模型中。在本文中，使用了卷积长短期记忆（ConvLSTM）层，以便对从CNN最后一个卷积层获得的特征图之间的空间依赖性进行编码，并提高模型的图像表示能力。此外，挤压激励（SE）块，一种空间注意力机制，用于为重要的局部特征分配权重。这两种机制应用于三种流行的CNN模型——VGG19、InceptionV3和MobileNet，以提高它们的分类能力。最后，基于Sugeno模糊积分的集成方法用于这些分类器的输出，以进一步提高检测精度。在实验中，考虑了三个在COVID-19检测中非常普遍的胸部X光数据集。对于所有这三个数据集，发现所提出的方法获得的结果与现有技术方法相当。代码以及预训练模型可在https://github.com/colabpro123/CovidConvLSTM上找到。
