C+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images based on CBAM and EfficientNet.
C+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images based on CBAM and EfficientNet.
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DOI：
10.1016/j.chaos.2021.111310
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发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Canayaz M
中科院分区：
文献类型：
--
作者：
Canayaz M
新冠肺炎，我们这个时代最大的疾病之一，继续在世界各地迅速传播。对该病的诊断和治疗的研究仍在迅速进行。将感染这种病毒的个人与社会其他人隔离，以防止疾病进一步传播，这一点非常重要。除了在患者的检测过程中进行的测试外，还使用了X射线和计算机断层扫描。在这项研究中，提出了一种新的混合模型，可以从使用当前深度学习模型之一的EfficientNet创建的CT图像中诊断新冠肺炎，该模型由注意块组成。在这个新模型的第一步中，使用通道注意、空间注意和残差块来从图像中提取最重要的特征。根据超列技术对提取的特征进行组合。在模型的第二步中，组合的特征被作为输入给EfficientNet模型。在特征选择后，使用支持向量机分类器对从该混合模型获得的深层特征进行分类。采用主成分分析方法进行特征选择。该方法能够对新冠肺炎进行准确预测，准确率达到99%。该方法使用了EfficientNet的前四个版本。此外，将贝叶斯优化方法应用于支持向量机分类器的超参数估计。给出了该方法与该领域其他方法的性能比较分析。
COVID-19, one of the biggest diseases of our age, continues to spread rapidly around the world. Studies continue rapidly for the diagnosis and treatment of this disease. It is of great importance that individuals who are infected with this virus be isolated from the rest of the society so that the disease does not spread further. In addition to the tests performed in the detection process of the patients, X-ray and computed tomography are also used. In this study, a new hybrid model that can diagnose COVID-19 from computed tomography images created using EfficientNet, one of the current deep learning models, with a model consisting of attention blocks is proposed. In the first step of this new model, channel attention, spatial attention, and residual blocks are used to extract the most important features from the images. The extracted features are combined in accordance with the hyper-column technique. The combined features are given as input to the EfficientNet models in the second step of the model. The deep features obtained from this proposed hybrid model were classified with the Support Vector Machine classifier after feature selection. Principal Components Analysis was used for feature selection. The approach can accurately predict COVID-19 with a 99% accuracy rate. The first four versions of EfficientNet are used in the approach. In addition, Bayesian optimization was used in the hyper parameter estimation of the Support Vector Machine classifier. Comparative performance analysis of the approach with other approaches in the field is given.
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