Deep learning and red deer optimiser for automatic cardiovascular disease identification on magnetic resonance images.
Authors
Affiliations (6)
Affiliations (6)
- Department of CSE, Moradabad Institute of Technology, Moradabad, Uttar Pradesh, India. [email protected].
- Department of CSE-AIML, Moradabad Institute of Technology, Moradabad, Uttar Pradesh, India.
- Department of CSE-AI, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India.
- Colleges of Computing Sciences & IT, Teerthanker Mahaveer University, Moradabad, India.
- Noida Institute of Engineering & Technology,, Greater Noida, India.
- Department of Computer Science and Engineering, School of Computing Science and Engineering, Galgotias University, Greater Noida, India.
Abstract
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.