AI-Based Heart Attack Risk Prediction usingMRI Datasets
DOI:
https://doi.org/10.15662/IJEETR.2026.0802443Keywords:
myocardial infarction detection, deep transfer learning, MobileNetV2, cardiac MRI analysis, convolutional neural network, FastAPI, threshold calibration, medical image classification, clinical decision supportAbstract
The escalating prevalence of cardiovascular disease in contemporary clinical settings has intensified the demand for rapid, reliable, and automated diagnostic systems capable of accurately identifying myocardial infarction from cardiac imaging data. Conventional rule-based detection frameworks, which depend on static pixel-intensity thresholds and manually engineered mathematical boundaries, demonstrate fundamental inadequacy when confronted with the inherent variability of Magnetic Resonance Imaging (MRI) outputs across heterogeneous clinical hardware environments. This paper presents a clinically oriented Cardiac Infarction Detection Suite built upon the MobileNetV2 Convolutional Neural Network (CNN) architecture and deployed via Transfer Learning, enabling robust spatial feature extraction from a constrained cardiac MRI dataset without succumbing to model collapse. The proposed system integrates advanced tensor preprocessing, data augmentation strategies, calibrated threshold logic, and an asynchronous FastAPI backend to achieve real-time inference with sub-two-second latency. A dynamic threshold calibration mechanism addresses dataset-inherent bias by repositioning the diagnostic boundary from the conventional 0.50 to a data-driven 0.20, significantly reducing false-positive classifications. Clinical outputs are surfaced through an interactive HTML frontend that generates color-coded, downloadable PDF radiology reports. Empirical evaluation demonstrates that the proposed Transfer Learning architecture substantially outperforms conventional pixel-mathematics approaches in terms of classification accuracy, contextual awareness, and robustness to MRI calibration variance, establishing a scalable, computationally efficient foundation for AI-assisted myocardial infarction triage in modern cardiology workflows.
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