Key result
Novel deep learning model outperforms existing techniques for cardiac MRI segmentation and disease classification.
Why the study?
Early detection of cardiovascular disease through analysis of left and right ventricle cavities and myocardium from cardiac MRI images is essential, and deep learning methods for segmentation and classification are gaining attention.
Does a deep join attention model combined with a region integrated deep residual network improve the accuracy of ventricle segmentation and multiclass cardiac disease detection on cardiac MRI?
Does a deep join attention model combined with a region integrated deep residual network improve the accuracy of ventricle segmentation and multiclass cardiac disease detection on cardiac MRI?
A novel deep learning architecture combining DJAM and RIDRN improves the automated segmentation and classification of cardiac diseases from MRI images.
May aid DL segmentation of CMR for early CVD detection; leaves open prospective validation before clinical adoption.
In this modern era, various cardiac diseases are very crucial as they cause a high mortality rate. Early detection of cardio vascular disease (CVD) is essential to prevent and control it. Diagnosis of cardiac disease is the process of analyzing the left and right ventricle cavities (LV, RV) and myocardium (MYO) from cardiac magnetic resonance (CMR) images. As deep learning architectures are becoming more mature, segmenting, and classifying cardiac MRI images using deep learning is gaining more attention. This work is aimed to identify five different cardiac disease subgroups namely NOR, MINF, DCM, HCM, and ARC by employing a new deep join attention model (DJAM) technique for segmenting LV, MYO, and RV regions separately. This method provides advancement as the joined attention model was combined with the pooling layers and the resultant is added to the convolution layers. The proposed region integrated deep residual network (RIDRN) is used to extract the features from the segmented images for classification. In this process, the features of LV, RV, and MYO are combined with a different combination. The advantage of doing this process is to get the overall features without leaving any single strip of features from the three regions. Hence, it shows a rise in performance accuracy. The random forest classification method is used to classify the underlying features for cardiac disease diagnosis. This proposed work is tested in the automated cardiac diagnosis challenge (ACDC) dataset and it perfects the state‐of‐art techniques.
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Velappan et al. (2023) studied Cardiac diseases (NOR, MINF, DCM, HCM, ARC). Deep join attention model (DJAM) and region integrated deep residual network (RIDRN) vs. State-of-the-art techniques was evaluated on Performance accuracy in segmenting ventricle regions and classifying cardiac diseases. A proposed deep join attention model and region integrated deep residual network for cardiac MRI segmentation and classification outperformed state-of-the-art techniques on the ACDC dataset.
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