A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection.

ABSTRACT: Myocardial infarction (MI) remains a significant contributor to global mortality and morbidity, necessitating accurate and timely diagnosis. Current diagnostic methods encounter challenges in capturing intricate patterns, urging the need for advanced automated approaches to enhance MI detection. In this study, we strive to advance MI detection by proposing a hybrid approach that combines the strengths of ResNet and Vision Transformer (ViT) models, leveraging global and local features for improved accuracy. We introduce a slim-model ViT design with multibranch networks and channel attention mechanisms to enhance patch embedding extraction, addressing ViT's limitations. By training data through both ResNet and modified ViT models, we incorporate a dual-pathway feature extraction strategy. Th

SUBMITTER: Wahid JA 

PROVIDER: S-EPMC10883929 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature