Abstract
These days, the number of people infected with the emerging coronavirus is mutated strain has increased, the demand for the COVID-19 test has increased. Many of the hospitals have used the PCR test, but this test has high false negatives cases. There is a need to use an alternative high-performance screening technology such as chest X-Rays. This paper provides a web application based on a deep learning technique to examine the X-ray image of patients. Deep learning has become the ability to detect different diseases by extracting features from images, such as anomalies from lung X-rays. The pre-training models, Inception-V3, InceptionResNetV2, and MobileNet, trained on the large dataset, consist of three images are;Pneumonia, Covid-19 and Normal; The classification results on validation data for each model are as follows: MobileNet 98.75%, InceptionResNetV2 97%, and InceptionV3 97%. The MobileNet achieves the highest accuracy. Therefore it was used to develop the web application for detecting COVID-19.
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Al-Madani, A.M., Gaikwad, A.T., Ahmed, Z.A.T., Mahale, V., Alsubari, S.N., Tawfik, M. (2022). Web Application Based on Deep Learning for Detecting COVID-19 Using Chest X-Ray Images. In: Choudhury, T., Katal, A., Um, JS., Rana, A., Al-Akaidi, M. (eds) Telemedicine: The Computer Transformation of Healthcare. TELe-Health. Springer, Cham. https://doi.org/10.1007/978-3-030-99457-0_18
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