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Automatic Detection of CoVID-19 Infection Using Chest X-Ray Images Through Transfer Learning

IEEE/CAA journal of Automatica Sinica 2021年第1期8卷 239-248页

作者： Elene Firmeza ohata Gabriel Maia Bezerra joão victor souza das chagas Aloísio Vieira Lira Neto Adriano Bessa Albuquerque victor Hugo Cde Albuquerque Pedro Pedrosa Rebouças Filho the Universidade Federal do Ceará FortalezaCE 60455-970and also with the Instituto Federal do CearáFortalezaCE 60040-215Brazil the Instituto Federal do Ceará FortalezaCE 60040-215Brazil the Universidade de Fortaleza FortalezaCE 60811-905Brazil

The new coronavirus(CoVID-19),declared by the World Health organization as a pandemic,has infected more than 1 million people and killed more than 50 *** infection caused by CoVID-19 can develop into pneumonia,which can be detected by a chest X-ray exam and should be treated *** this work,we propose an automatic detection method for CoVID-19 infection based on chest X-ray *** datasets constructed for this study are composed of194 X-ray images of patients diagnosed with coronavirus and 194 X-ray images of healthy *** few images of patients with CoVID-19 are publicly available,we apply the concept of transfer learning for this *** use different architectures of convolutional neural networks(CNNs)trained on Image Net,and adapt them to behave as feature extractors for the X-ray ***,the CNNs are combined with consolidated machine learning methods,such as k-Nearest Neighbor,Bayes,Random Forest,multilayer perceptron(MLP),and support vector machine(SVM).The results show that,for one of the datasets,the extractor-classifier pair with the best performance is the Mobile Net architecture with the SVM classifier using a linear kernel,which achieves an accuracy and an F1-score of 98.5%.For the other dataset,the best pair is Dense Net201 with MLP,achieving an accuracy and an F1-score of 95.6%.Thus,the proposed approach demonstrates efficiency in detecting CoVID-19 in X-ray images.

关键词： Convolutional neural networks(CNNs) CoVID-19 transfer learning X-ray

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