Deep-Learning-Based Coronary Artery Calcium Detection from CT Image
| Autor: | Ahyoung Lee, Hyo-Wook Gil, Beanbonyka Rim, Min Hong, Sung-Shick Jou, Sungjin Lee, Xibin Jia |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Diagnostic methods chemistry.chemical_element TP1-1185 Calcium Biochemistry Residual neural network Article Analytical Chemistry Coronary artery disease calcium detection Medicine Electrical and Electronic Engineering Instrumentation Contextual image classification business.industry Coronary artery calcium score Deep learning Chemical technology deep learning VGG inception resnet V2 resnet-50 medicine.disease Coronary Vessels Atomic and Molecular Physics and Optics Coronary artery calcium chemistry coronary artery calcium score CT Artificial intelligence Radiology Neural Networks Computer business Tomography X-Ray Computed image classification |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 21 Issue 21 Sensors, Vol 21, Iss 7059, p 7059 (2021) |
| ISSN: | 1424-8220 |
| Popis: | One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible. |
| Databáze: | OpenAIRE |
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