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COVID-19 Detection from CT Scan Images using Transfer Learning Approach

Authors:

Navjot Singh Bajaj,

Pavinder Yadav,

Nidhi GuptaAuthors Info & Claims

ICMLSC '24: Proceedings of the 2024 8th International Conference on Machine Learning and Soft Computing

Pages 152 - 157

https://doi.org/10.1145/3647750.3647774

Published: 12 April 2024 Publication History

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Abstract

In the past years, since 2020, the outbreak of COVID-19 has alarmed the world with the speed and its spread around the world. This raised the demand of early, accurate and automated detection system for the COVID-19 as there is a scarcity of manpower in medical field. This attracted many researches using deep learning to build COVID-19 detection model. For the diagnosis of COVID-19, computed tomography scanning are being used as more accurate, non-invasive and efficient method in real-time. In this work, we have proposed a model using six different image classification techniques of deep learning on CT scan images and compared the accuracy to find the most suitable and reliable model for transfer learning to achieve best result on ResNet50 as 97.19% training and 98.05% testing accuracy. The model will automate the process of detection of the COVID-19, leading to the advancement in the field of smart health-care.

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Cited By

Hanafi M(2024)AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task PerformanceBig Data and Cognitive Computing10.3390/bdcc80700778:7(77)Online publication date: 9-Jul-2024
https://doi.org/10.3390/bdcc8070077
Kumar VAli RSharma P(2024)IoEPM+: A secured and lightweight 6G-enabled pollution monitoring authentication framework using IoT and blockchain technologyComputer Networks10.1016/j.comnet.2024.110554250(110554)Online publication date: Aug-2024
https://doi.org/10.1016/j.comnet.2024.110554

Index Terms

COVID-19 Detection from CT Scan Images using Transfer Learning Approach
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ICMLSC '24: Proceedings of the 2024 8th International Conference on Machine Learning and Soft Computing

January 2024

210 pages

ISBN:9798400716546

DOI:10.1145/3647750

Copyright © 2024 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 12 April 2024

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ICMLSC 2024

ICMLSC 2024: 2024 The 8th International Conference on Machine Learning and Soft Computing

January 26 - 28, 2024

Singapore, Singapore

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Cited By

Hanafi M(2024)AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task PerformanceBig Data and Cognitive Computing10.3390/bdcc80700778:7(77)Online publication date: 9-Jul-2024
https://doi.org/10.3390/bdcc8070077
Kumar VAli RSharma P(2024)IoEPM+: A secured and lightweight 6G-enabled pollution monitoring authentication framework using IoT and blockchain technologyComputer Networks10.1016/j.comnet.2024.110554250(110554)Online publication date: Aug-2024
https://doi.org/10.1016/j.comnet.2024.110554

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