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The world’s socioeconomic structure has been disrupted by COVID-19 which has turned into a global catastrophe. Effective and economical diagnosis techniques are crucial for improving COVID-19 treatment outcomes and removing misleading results. The analysis of lung CT is becoming increasingly important for diagnosing COVID-19, a disease that primarily affects the lungs. As a result, the proposed work introduces the Linear Convolutional Network Model (LCNM), a model for detecting COVID-19. The model was trained on CT images that fall into two categories: Positive and negative. Area Under Curve (AUC) is extracted using an image pre-processing pipeline so that the input contains the necessary features. The LCNM method achieves an accuracy of 95% and an F1-score of 97%. The anticipated model gives superior performance compared to the prevailing methods. These promising findings are expected to expedite the advancement of deep learning-based COVID-19 diagnostic systems that rely on radiography.

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Provisioning Linear Convolutional Network Model-Based Image Denoising Model for Covid Prediction Using X-Ray Images

  • 会议论文

  • 发表日期：

    2025年

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  • A. S. Vinay Raj,
  • Malyala Gayatri,
  • N. Gopinath,
  • S. Vijayalakshmi,
  • S. Vijayakumar,
  • S. Thilagavathy

摘要

The world’s socioeconomic structure has been disrupted by COVID-19 which has turned into a global catastrophe. Effective and economical diagnosis techniques are crucial for improving COVID-19 treatment outcomes and removing misleading results. The analysis of lung CT is becoming increasingly important for diagnosing COVID-19, a disease that primarily affects the lungs. As a result, the proposed work introduces the Linear Convolutional Network Model (LCNM), a model for detecting COVID-19. The model was trained on CT images that fall into two categories: Positive and negative. Area Under Curve (AUC) is extracted using an image pre-processing pipeline so that the input contains the necessary features. The LCNM method achieves an accuracy of 95% and an F1-score of 97%. The anticipated model gives superior performance compared to the prevailing methods. These promising findings are expected to expedite the advancement of deep learning-based COVID-19 diagnostic systems that rely on radiography.