High Performance Hybrid CNN CBAM Framework for High Sensitivity Heart Disease Classification

Abstract

Cardiovascular diseases remain a paramount global health crisis, necessitating early and precise diagnostic interventions. While medical imaging is the clinical standard, manual interpretation is highly susceptible to visual fatigue and inter-observer variability. This study proposes a novel, highly robust Computer-Aided Diagnosis (CAD) framework that overcomes the spatial and textural limitations of standalone Convolutional Neural Networks (CNN) in heart disease image classification. A Feature-Level Ensemble (Hybrid) architecture was created by putting together the deep semantic features of ResNet50V2, the spatial boundaries of VGG16, and the parameter efficiency of EfficientNetV2B3. To directly deal with the loss of features caused by anatomical background noise, a Convolutional Block Attention Module (CBAM) was added to the EfficientNet pathway. This gave the network two-dimensional (channel and spatial) visual attention. To guarantee a thorough and impartial assessment, a complete restructuring of a dataset comprising 5,977 images was undertaken using an 80:10:10 stratified split, thereby eliminating the accuracy paradox resulting from class imbalance. The proposed Hybrid CBAM model significantly outperforms standalone baselines, with a peak accuracy of 94.00%. For clinical use, it was very important that the attention-guided ensemble had a Recall (sensitivity) of 0.94 for finding pathological cases and a Negative Precision of 0.96. This study definitively demonstrates that the integration of multi-model feature extraction with focused visual attention mechanisms yields a highly sensitive, reliable, and non-invasive automated screening instrument for the early detection of cardiovascular disease.