Cardiac Risk Assessment Through Retinal Images
Cardiovascular disease (CVD) is the leading cause of death across the globe. Therefore, detection at an early stage is all the more crucial. In this project, retinal images captured during a routine eye examination are used for the prediction of heart attack by deep learning and machine learning. Im...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/05/itmconf_iccp-ci2024_01007.pdf |
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| Summary: | Cardiovascular disease (CVD) is the leading cause of death across the globe. Therefore, detection at an early stage is all the more crucial. In this project, retinal images captured during a routine eye examination are used for the prediction of heart attack by deep learning and machine learning. Improvement in quality of images, highlights on blood vessels, extracting meaningful features like shape of vessel, and density that relates heart health. The approach will be hybrid, based on a combination of a neural classifier RNN using clustering and AdaBoost for generating highly accurate predictive outputs as well as providing an assessment score for potential issues concerning heart conditions. It is a non-invasive cost-effective procedure, fast, easy, and may be conducted along with regular eye checks to encourage early intervention and better health. |
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| ISSN: | 2271-2097 |