MRI‐Based Heart Disease Diagnosis: An Automated IBNet9X‐DenseNet8X Framework for Optimal Feature Fusion and Classification

ABSTRACT Cardiovascular diseases (CVDs) remain a leading global health challenge, emphasising the need for advanced diagnostic systems that enable accurate detection and classification. This study presents a novel deep learning framework based on a dense and inverted bottleneck residual mechanism, integrating two custom convolutional neural network architectures: an 8‐block Dense model and a 9‐block Inverted Bottleneck Layered model. These models are designed to extract multiscale features from cardiac MRI scans for precise classification across five CVD categories. We enhance image quality using Contrast‐Limited Adaptive Histogram Equalisation (CLAHE) in the preprocessing pipeline and apply data augmentation to address class imbalance and promote model generalisation. Atom Search optimisation (ASO) is employed to reduce feature dimensionality while retaining critical information, and a serial feature fusion strategy is used to integrate the optimised feature vectors. Classification is performed using several neural network variants (Narrow NN, Medium NN, Wide NN, and Bi‐Layered NN). The proposed method is evaluated on both the Automated Cardiac Diagnosis Challenge (ACDC) and Sunnybrook Cardiac Data (SCD) datasets, achieving superior accuracies of 97.3% and 96.8%, respectively, compared to state‐of‐the‐art techniques, demonstrating its effectiveness for cardiovascular disease diagnosis.