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• W4210361996 abstract "The early diagnosis and treatment of COVID-19 has been a challenge all over the world. It is challenging to manufacture many testing kits and even then, their accuracy rate is very low. Studies carried out recently show that chest x-ray images are of great help in the diagnosis of COVID-19. In this study, we have developed a COVID-19 detection model that by observing the chest x-ray images of the patient, detects that either the patient is affected by COVID-19 or not. The model is developed using a custom Convolutional Neural Network (CNN) that differentiates between COVID-19 and healthy x-ray images so that the patient can be diagnosed and quarantined on time to prevent the spread of the pandemic. We used two different datasets which are publicly available for the training and validation of this model. Upon completion, the proposed model yields an accuracy of almost 98&#x0025;. Upon further training, our model will be able to be used as a COVID-19 detection tool in hospitals worldwide and will play a vital role in early detection and timely containment of the pandemic." @default.
• W4210361996 created "2022-02-08" @default.
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• W4210361996 date "2021-11-09" @default.
• W4210361996 modified "2023-10-16" @default.
• W4210361996 title "Covid-19 Detection by using Deep learning-based Custom Convolution Neural Network (CNN)" @default.
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• W4210361996 doi "https://doi.org/10.1109/icic53490.2021.9693071" @default.
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