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AlexNet for Image-Based COVID-19 Diagnosis

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Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023) (MICAD 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1166))

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Abstract

The medical community is working harder to develop quick and accurate methods of diagnosing the virus in response to the COVID-19 pandemic. The speed and efficiency of image-based COVID-19 diagnosis are its benefits, but it also carries a risk of error and necessitates the involvement of numerous skilled radiologists. In this paper, a novel convolutional neural network architecture called AlexNet is presented. It has the capacity to automatically learn features in a hierarchy and recognize complex patterns and improves the model’s recognition of disease-related features. In addition, AlexNet’s adaptability and generalization capabilities contribute to its effectiveness in processing various imaging datasets. AlexNet therefore has great potential to identify complex patterns associated with COVID-19-related lung abnormalities. Nevertheless, it also has certain limitations, including the need for a large amount of processing power, the possibility of overfitting, the lack of sufficient interpretability, and the need for further development in order to make it more applicable to particular diagnostic tasks. In summary, collaborative efforts between the AI research community and healthcare professionals will continue to seek accurate, efficient, and ethical solutions for image-based COVID-19 diagnosis.

Y. Peng—Contributed equally to this work.

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Acknowledgments

The research work was supported by the open project of State Key Laboratory of Millimeter Waves (Grant No. K202218).

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Authors and Affiliations

  1. School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, 211200, China

    Min Tang, Yibin Peng & Shuwen Chen

  2. Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China

    Shuihua Wang

  3. School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK

    Yudong Zhang

  4. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Yudong Zhang

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  1. Min Tang
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  2. Yibin Peng
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  3. Shuihua Wang
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Correspondence to Shuwen Chen or Yudong Zhang .

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Editors and Affiliations

  1. Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China

    Ruidan Su

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. University of Manchester, Manchester, UK

    Alejandro F. Frangi

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Tang, M., Peng, Y., Wang, S., Chen, S., Zhang, Y. (2024). AlexNet for Image-Based COVID-19 Diagnosis. In: Su, R., Zhang, YD., Frangi, A.F. (eds) Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). MICAD 2023. Lecture Notes in Electrical Engineering, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-97-1335-6_16

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  • DOI: https://doi.org/10.1007/978-981-97-1335-6_16

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