COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
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DOI：
10.1016/j.compbiomed.2020.103805
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发表时间：
2020-06-01
影响因子：
7.7
通讯作者：
Comert, Zafer
中科院分区：
文献类型：
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作者：
Togacar, Mesut;Ergen, Burhan;Comert, Zafer
冠状病毒引起各种呼吸道感染，它是一种RNA型病毒，可以感染人类和动物物种。它经常导致人类肺炎。人工智能模型有助于生物医学领域的成功分析。在这项研究中，冠状病毒是使用深度学习模型检测的，这是人工智能的一个分支。我们的数据集包括三类，即：冠状病毒，肺炎和正常X射线图像。在这项研究中，使用模糊颜色技术作为预处理步骤的数据类进行重组，并与原始图像结构化的图像进行堆叠。在下一步中，使用深度学习模型（MobileNetV 2，SqueezeNet）训练堆栈数据集，并使用Social Mimic优化方法处理模型获得的特征集。此后，有效的功能组合和分类使用支持向量机（SVM）。所提出的方法获得的总体分类率为99.27%。通过本研究中提出的方法，很明显，该模型可以有效地有助于COVID-19疾病的检测。
Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.