CDSS for Automated Cardiac MRI Diagnosis Using an Explainable Ensemble Deep Learning Model
TL;DR
It is demonstrated that architectural diversity combined with probabilistic aggregation constitutes an effective and interpretable strategy for reliable cardiac MRI diagnosis in clinical decision support systems.
Abstract
Automated classification of cardiac pathologies from cine-MRI remains a clinically significant challenge due to inter-patient morphological variability. This study presents a comparative evaluation of deep learning architectures and proposes an explainable ensemble framework for cardiac disease classification using the publicly available ACDC dataset. Seven pre-trained models were fine-tuned via transfer learning VGG16, MobileNet, EfficientNet, GoogLeNet, ResNet18, DenseNet, and Vision Transformer (ViT) on 1,468 cine-MRI images (80/20 train-test split). A soft voting ensemble combining the top-performing architectures was developed to improve generalization and diagnostic robustness. Among individual models, VGG16 achieved the strongest performance (accuracy: 97.28%, F1-score: 0.9643, precision: 0.9561, recall: 0.9726, specificity: 0.9726). The proposed ensemble model consistently outperformed all standalone architectures, yielding 98.23% accuracy, F1-score of 0.9762, precision of 0.9753, recall of 0.9771, and specificity of 0.9771, with a clinically relevant reduction in false negatives and an AUC of 0.9971. Explainability was ensured through complementary post-hoc analyses using SHAP, LIME, and Grad-CAM, collectively confirming anatomically coherent and clinically meaningful decision patterns. These results demonstrate that architectural diversity combined with probabilistic aggregation constitutes an effective and interpretable strategy for reliable cardiac MRI diagnosis in clinical decision support systems.