Accurate COVID-19 Detection using Xception with X-ray Images
Keywords:
COVID-19, Confusion matrix, Deep neural network, False negatives, X-ray imagesAbstract
The outbreak of coronavirus complaint is generally known as COVID-19. It has a major impact on human health and their routine life in various countries. Early discovery of COVID-19 through accurate detection and observation of the original point of infection in the case of COVID-19 is essential to control the disease and reduce its rapid-fire spreading among people worldwide. This helps to diminish the death rate. In the health care unit, primarily X-ray has been used to test the patients. The proposed system aimed at the structure of different trained Deep literacy models grounded on Convolutional Neural Networks (CNNs) for the spontaneous exposure of COVID-19 using X-rays. The ease of diagnosing the cases requires detecting the complaint from radiography images. The earlier studies on this content include machine literacy strategies for COVID-19 using casket X-rays showed good delicacy and discovery rate. Presently, the RTPCR test is the most generally used fashion to discover the complaint of COVID-19. But the major problem occurs when it has a high false rate, and the time for carrying the results is also high. In this research, we proposed a COVID-19 detection model using Deep Convolutional Neural network (DCNN) algorithms using the X-ray images. According to the research, it is found that Deep CNN models are producing the most accurate results for COVID-19 detection. The use of this DCNN model in analyzing X-ray images in the proposed model is producing 98% accurate results in finding COVID or Non-COVID.
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