Myocardial Infarction Detection Through Multiview Echocardiography Optimizing Deep Learning-Based Models
Myocardial Infarction Detection Through Multiview Echocardiography Optimizing Deep Learning-Based Models is a cutting-edge research project focused on improving the early detection of heart attacks (Myocardial Infarction) using Artificial Intelligence. The proposed framework leverages multiview echocardiography images and state-of-the-art deep learning models to enhance diagnostic accuracy, providing a reliable decision-support system for healthcare professionals. This work contributes to the advancement of AI-assisted cardiovascular diagnosis and demonstrates the potential of deep learning in medical imaging applications.
Key Highlights
- Research Area: Medical AI & Healthcare
- Technology: Deep Learning, Computer Vision, Medical Image Analysis
- Application: Automated Myocardial Infarction Detection
- Input Data: Multiview Echocardiography Images
- Objective: Improve diagnostic accuracy and support early clinical decision-making
- Published In: IEEE
- Journal Ranking: Q1
- Impact Factor: 4.2
- CiteScore: 9.3
Technologies Used
- Python
- TensorFlow
- Keras
- Deep Learning
- CNN Architectures
- Medical Image Processing
- Echocardiography Analysis
Publication Information
Title: Myocardial Infarction Detection Through Multiview Echocardiography Optimizing Deep Learning-Based Models
Publisher: IEEE
Journal Ranking: Q1
Impact Factor: 4.2
CiteScore: 9.3
Paper Link: https://ieeexplore.ieee.org/document/11576051
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