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- W3200726529 abstract "At the end of 2019, a new type of virus called SARS-CoV-2 began spreading resulting in a global pandemic. As of June 2021, almost 175 million people were affected worldwide. Symptom-wise, it is very difficult to diagnose if a person has Covid or just a viral infection. But, taking a close look at chest X-Rays is extremely helpful in the diagnostic process. The proposed methodology in this paper helps in classification of chest X-Ray images into 3 categories: ‘Covid’, ‘Viral’ and ‘Normal’. The dataset was created by integrating 3 pre-existing evergrowing datasets and the ResNet-18 model was adopted to train it. The experimental results show that the classification of the chest X-Ray images was done with an accuracy of 0.9648. An adversarial machine learning approach was employed to poison the train data after which the classification accuracy dropped to 0.8711." @default.
- W3200726529 created "2021-09-27" @default.
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- W3200726529 date "2021-01-01" @default.
- W3200726529 modified "2023-10-15" @default.
- W3200726529 title "COVID-19 Diagnosis from Chest X-Ray Images Using Convolutional Neural Networks and Effects of Data Poisoning" @default.
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- W3200726529 doi "https://doi.org/10.1007/978-3-030-87013-3_38" @default.
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