What does this research mean for the field?

An Automated Cardiovascular Disease Detection framework combining an Attention-Based Convolutional Gated Recurrent Unit network with the Red Deer Optimiser achieves higher classification accuracy and computational efficiency than existing approaches for identifying cardiovascular diseases on MRI. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to enhance automatic identification of cardiovascular diseases using deep learning and MRI.

June 29, 2026Scientific Reports2 citationsOpen Access

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

Q: What is the clinical evidence from this study?

Study design: Other. Population: Cardiovascular disease. Intervention: Automated Cardiovascular Disease Detection framework (ACVD-RDODL) vs. Existing machine learning approaches. Primary outcome: Classification accuracy.

ASArun Kumar Singh ASArun Kumar Singh HAHimanshu Agarwal

Resultado clave

The proposed Automated Cardiovascular Disease Detection framework (ACVD-RDODL) combining deep learning with the Red Deer Optimiser improved classification accuracy and computational efficiency for cardiovascular disease identification on MRI.

Puntos clave

This study aims to enhance automatic identification of cardiovascular diseases using deep learning and MRI.
Proposed an Automated Cardiovascular Disease Detection framework (ACVD-RDODL).
Applied image enhancement methods including Wiener Filtering (WF) and Dynamic Histogram Equalization (DHE).
Used Attention-Based Convolutional Gated Recurrent Unit (ACGRU) for classification and Red Deer Optimiser (RDO) for hyperparameter optimization.
The proposed method achieved superior classification accuracy compared to existing approaches.
Demonstrated improved computational efficiency in processing cardiac MRI data.

PICO estructurado

Does the ACVD-RDODL framework improve classification accuracy and computational efficiency in cardiovascular disease identification on MRI compared to existing approaches?

Población

Benchmark cardiac MRI dataset

Intervención

Automated Cardiovascular Disease Detection framework (ACVD-RDODL) combining deep learning (Attention-Based Convolutional Gated Recurrent Unit network) with Red Deer Optimiser (RDO)

Comparador

Existing approaches

Resultado

Classification accuracy and computational efficiencysurrogate

A novel deep learning framework combining ACGRU and Red Deer Optimiser improves the automated detection of cardiovascular diseases on MRI.

Limitaciones

Small and unbalanced datasets limit generalizability
Lack of interpretability of deep learning predictions

Resumen

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.

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Cite This Study

Singh et al. (Sat,) conducted a other in Cardiovascular disease. Automated Cardiovascular Disease Detection framework (ACVD-RDODL) vs. Existing machine learning approaches was evaluated on Classification accuracy. The proposed Automated Cardiovascular Disease Detection framework (ACVD-RDODL) combining deep learning with the Red Deer Optimiser improved classification accuracy and computational efficiency for cardiovascular disease identification on MRI.

synapsesocial.com/papers/6a420ba8f91bb43ea9192a85 https://doi.org/https://doi.org/10.1038/s41598-026-55595-6

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