Abstract
The rise of the COVID-19 pandemic has drastically changed the world. The World Health Organization (WHO) declared this disease as a pandemic owing to its fast rise across the globe. At present, the most widely used diagnostic test used for detection of COVID-19 is the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test, which assesses the level of Ribonucleic Acid (RNA) and surface protein in a given sample. This is a costly and time-consuming test. Therefore, the use of an automated detection system would be a fast and efficient alternative for the diagnosis and prevention of COVID-19 among populations. The proposed work uses the X-rays’ images of the lungs of patients to classify whether an X-ray is healthy or COVID-19 affected. The experiment was performed for every category data sample and classification is done through deep learning using convolutional neural network (CNN), which gives instant results with an accuracy of 96%.
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Kirar, B.S., Tilwankar, S.S., Paliwal, A., Sharma, D., Agrawal, D.K. (2023). Detection of COVID-19-Affected Persons Using Convolutional Neural Network from X-Rays’ Images. In: Sisodia, D.S., Garg, L., Pachori, R.B., Tanveer, M. (eds) Machine Intelligence Techniques for Data Analysis and Signal Processing. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore. https://doi.org/10.1007/978-981-99-0085-5_60
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DOI: https://doi.org/10.1007/978-981-99-0085-5_60
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