Cite this DOI

S. Badve, S., M. Tapase Tapase, D., & Bangale, A. S. (2021). Covid-19 Prediction Using Deep Convolutional Neural Networks. *Journal of Science & Technology*, *06*(01), 345–351. https://doi.org/10.46243/jst.2021.v6.i04.pp345-351

@article{sbadve2021covid19,
  author    = {S. Badve, Shantanu and M. Tapase Tapase, Dhanraj and Bangale, Abhishek S.},
  title     = {{Covid-19 Prediction Using Deep Convolutional Neural Networks}},
  journal   = {Journal of Science \& Technology},
  year      = {2021},
  month     = {aug},
  volume    = {06},
  number    = {01},
  pages     = {345--351},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2021.v6.i04.pp345-351},
  url       = {https://doi.org/10.46243/jst.2021.v6.i04.pp345-351},
  language  = {en},
  abstract  = {The currently available methods such as RT-PCR for the detection of the novel coronavirus disease fail due to restricted supply of test kits and few favourable signs of illness in the early phases, requiring the use of alternative solutions. The use of an Artificial Intelligence (AI) tool, could assist the world in developing an additional disease prevention regulation. An automatic detection technique is provided in this method, which leverages information from Computer Tomography (CT) images to train the deep learning model CNN architecture. CNNs are the best deep learning model option due to its promising accuracy for biomedical images and availability of fewer samples, which satisfies the need for CNN training. The presented paper aims to discuss the various aspects of the system, beginning with a brief summary and gradually progressing to explain the various implementations, which include the datasets used, the use of the State of the Art (SOTA), and a discussion of the various metrics used for evaluation. Finally, a user-interactive system is presented that employs the qualified model in the area.}
}

TY  - JOUR
TI  - Covid-19 Prediction Using Deep Convolutional Neural Networks
AU  - S. Badve, Shantanu
AU  - M. Tapase Tapase, Dhanraj
AU  - Bangale, Abhishek S.
JO  - Journal of Science & Technology
PY  - 2021
DA  - 2021/08/16/
VL  - 06
IS  - 01
SP  - 345
EP  - 351
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - The currently available methods such as RT-PCR for the detection of the novel coronavirus disease fail due to restricted supply of test kits and few favourable signs of illness in the early phases, requiring the use of alternative solutions. The use of an Artificial Intelligence (AI) tool, could assist the world in developing an additional disease prevention regulation. An automatic detection technique is provided in this method, which leverages information from Computer Tomography (CT) images to train the deep learning model CNN architecture. CNNs are the best deep learning model option due to its promising accuracy for biomedical images and availability of fewer samples, which satisfies the need for CNN training. The presented paper aims to discuss the various aspects of the system, beginning with a brief summary and gradually progressing to explain the various implementations, which include the datasets used, the use of the State of the Art (SOTA), and a discussion of the various metrics used for evaluation. Finally, a user-interactive system is presented that employs the qualified model in the area.
DO  - 10.46243/jst.2021.v6.i04.pp345-351
UR  - https://doi.org/10.46243/jst.2021.v6.i04.pp345-351
ER  -

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    "URL": "https://doi.org/10.46243/jst.2021.v6.i04.pp345-351",
    "title": "Covid-19 Prediction Using Deep Convolutional Neural Networks",
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    "abstract": "The currently available methods such as RT-PCR for the detection of the novel coronavirus disease fail due to restricted supply of test kits and few favourable signs of illness in the early phases, requiring the use of alternative solutions. The use of an Artificial Intelligence (AI) tool, could assist the world in developing an additional disease prevention regulation. An automatic detection technique is provided in this method, which leverages information from Computer Tomography (CT) images to train the deep learning model CNN architecture. CNNs are the best deep learning model option due to its promising accuracy for biomedical images and availability of fewer samples, which satisfies the need for CNN training. The presented paper aims to discuss the various aspects of the system, beginning with a brief summary and gradually progressing to explain the various implementations, which include the datasets used, the use of the State of the Art (SOTA), and a discussion of the various metrics used for evaluation. Finally, a user-interactive system is presented that employs the qualified model in the area.",
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2021.v6.i04.pp345-351 gives all four in one JSON answer.