IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization.
CK, R. & H, S. (2026). IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization.. Scientific reports. https://doi.org/10.1038/s41598-026-57536-9
CK R, H S. IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization.. Scientific reports. 2026; doi: 10.1038/s41598-026-57536-9
CK R, H S. IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization.[J]. Scientific reports. 2026. DOI: 10.1038/s41598-026-57536-9.
@article{ck2026,
author = {Revathi CK and Santhi H},
title = {IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization.},
journal = {Scientific reports},
year = {2026},
doi = {10.1038/s41598-026-57536-9},
note = {PMID: 42331954},
}
TY - JOUR AU - Revathi CK AU - Santhi H TI - IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization. T2 - Scientific reports PY - 2026 DO - 10.1038/s41598-026-57536-9 AN - PMID:42331954 ER -
Coronary artery disease(CAD) is a serious health issue worldwide. Early identification of CAD is used to prevent several complications, such as myocardial infarction and unexpected death. In existing studies, InceptionV3 is computationally intensive and struggles with long-range dependencies, whereas MobileNetV2 faces challenges in extracting intricate features from medical-image data. Similarly, U-NetR, despite its transformer-based encoding, requires large datasets for optimal performance and is computationally expensive because of its self-attention mechanism. To overcome these limitations, this study focuses on merging InceptionV3, U-NetR, and MobileNetV2 to enhance CAD classification performance. This approach involves utilizing pre-trained models and fine-tuning them using an angiographic dataset. The hybrid IUM model incorporates dynamic weighting to maximize prediction accuracy. Furthermore, this study employed VS Grad-CAM visualization to elucidate the classifier decisions using precise heatmaps, thereby improving interpretability. This method achieved exceptional diagnostic metrics: 0.97 accuracy, 0.99 F1-score, 0.98 specificity, and 0.97 sensitivity. This novel approach enhances diagnostic precision, minimizes manual errors, and facilitates real-time applications, making it a scalable and efficient solution for clinical application. Its prompt and accurate identification of CAD has the potential to enhance patient outcomes and optimize healthcare.