CONTRIBUTION OF RESNET34 IN THE DETECTION OF COVID-19, USING CHEST X-RAYS.
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Abstract
In the fight against the advance of COVID-19, chest X-ray images become an alternative in the detection of the virus. Interpreting radiographic films for virus detection is sometimes complicated by the presence of other respiratory diseases. A convolutional neural network with RESNET34 architecture is proposed as a complement in the detection of the virus, which allows classifying the images of chest radiographs with COVID-19 and NOCOVID images. The model presents a degree of reliability of 94.78% in the prediction of the images in the two classes COVID-19 and NOCOVID and an error in the prediction of the images of 5.22%. The results obtained show a high degree of prediction and classification of COVID cases above 90%, demonstrating the contribution of artificial intelligence in the development of preventive technologies for respiratory diseases, as well as support for medical personnel.
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