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Novel Architecture for Image Classification Based on Rough Set

Author

Listed:
  • S. Nivetha

    (Department of Computer Science, Periyar University, Salem, India)

  • H. Hannah Inbarani

    (Department of Computer Science, Periyar University, Salem, India)

Abstract

The Computed Tomography (CT) scan images classification problem is one of the most challenging problems in recent years. Different medical treatments have been developed based on the correctness of CT scan images classification. In this work, a novel deep learning architecture is proposed to correctly diagnose COVID-19 patients using CT scan images. In fact, a new classifier based on rough set theory is suggested. Extensive experiments showed that the novel deep learning architecture provides a significant improvement over well-known classifier. The new classifier produces 95% efficiency and a very low error rate on different metrics. The suggested deep learning architecture coupled with novel tolerance outperforms the other standard classification approaches for the detection of COVID-19 using CT-Scan images.

Suggested Citation

  • S. Nivetha & H. Hannah Inbarani, 2023. "Novel Architecture for Image Classification Based on Rough Set," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 14(1), pages 1-38, January.
    • Handle: RePEc:igg:jssmet:v:14:y:2023:i:1:p:1-38
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    References listed on IDEAS

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    1. Shifei Ding & Zhongzhi Shi & Ke Chen & Ahmad Taher Azar, 2015. "Mathematical Modeling and Analysis of Soft Computing," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-2, March.
    2. Heidi Ledford & David Cyranoski & Richard Van Noorden, 2020. "The UK has approved a COVID vaccine — here’s what scientists now want to know," Nature, Nature, vol. 588(7837), pages 205-206, December.
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