Optimizing Coronary Artery Disease Detection Using a New Triple Concatenated Convolution Neural Network

Coronary artery disease (CAD) is a pathological condition that is often fatal and is the main cause of death throughout the world.Early detection of this disease is very important to avoid severe complications such as heart attacks and sudden death.This study employs artificial intelligence, specifically deep learning via Convolutional Neural Networks (CNNs), to enhance CAD detection.While CNN architectures like ResNet50V2 and MobileNetV2 exhibit satisfactory performance individually, they possess distinct strengths and weaknesses.ResNet50V2 requires significant computing resources, hindering its scalability, while MobileNetV2 struggles with extracting complex features from medical images.Therefore, this research aims to combine the EfficientNetV2B0, ResNet50V2, and MobileNetV2 using transfer learning techniques to enhance CAD detection.The methodology involves leveraging pre-trained models and fine-tuning them on a coronary artery disease dataset.Modified models, particularly EfficientNetV2B0 and MobileNetV2, achieve high accuracies of 94% and 86%, respectively, while ResNet50V2 yields 72%.However, combining the models boosts accuracy to 95%, addressing individual model limitations.The concatenated model demonstrates superior predictive capabilities, with more accurate predictions and fewer errors than individual models.