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<p>Magnetic Resonance Imaging (MRI) is a non-invasive imaging method that can give detailed visualization of the cardiac structures and blood flow, which is effective in diagnosis of cardiovascular diseases (CVDs). It has been proposed that the combination of deep learning (DL) with MRI has an improved ability to automatically identify cardiovascular anomalies by identifying intricate patterns in large-scale imaging data. In this study, an Automated Cardiovascular Disease Detection framework (ACVD-RDODL) is proposed, which combines deep learning with the Red Deer Optimiser (RDO). After image enhancement methods like Wiener Filtering (WF) and Dynamic Histogram Equalization (DHE), features are extracted using radiomics. An Attention-Based Convolutional Gated Recurrent Unit (ACGRU) network is considered to ensure proper classification and RDO is used to optimize the hyperparameters and improve the performance of the progress model. As experimental testing of a benchmark cardiac MRI dataset shows, the proposed method is greater to the existing approaches in terms of classification accuracy and computational efficiency.</p>

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Deep learning and red deer optimiser for automatic cardiovascular disease identification on magnetic resonance images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月27日

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  • Ajeet Singh,
  • Himanshu Agarwal,
  • Atul Pratap Singh,
  • Amit Kumar,
  • Saurabh Srivastava,
  • Sudheer Kumar Singh

摘要

Magnetic Resonance Imaging (MRI) is a non-invasive imaging method that can give detailed visualization of the cardiac structures and blood flow, which is effective in diagnosis of cardiovascular diseases (CVDs). It has been proposed that the combination of deep learning (DL) with MRI has an improved ability to automatically identify cardiovascular anomalies by identifying intricate patterns in large-scale imaging data. In this study, an Automated Cardiovascular Disease Detection framework (ACVD-RDODL) is proposed, which combines deep learning with the Red Deer Optimiser (RDO). After image enhancement methods like Wiener Filtering (WF) and Dynamic Histogram Equalization (DHE), features are extracted using radiomics. An Attention-Based Convolutional Gated Recurrent Unit (ACGRU) network is considered to ensure proper classification and RDO is used to optimize the hyperparameters and improve the performance of the progress model. As experimental testing of a benchmark cardiac MRI dataset shows, the proposed method is greater to the existing approaches in terms of classification accuracy and computational efficiency.