Coronary Artery Disease Diagnosis: A Deep Learning Approach for CAD Detection in CT Imaging
Keywords:
CNN, Coronary Artery Disease, Deep Learning, Visual Geometry GroupAbstract
Heart disease is one of the leading global causes of death. Among them coronary artery disease contributing highest number of deaths it occurs when the main artery named coronary artery which supplies oxygen rich blood and many other nutrients to the heart gets thicker and narrower due to accumulation of fatty deposits a substance called atheroma is responsible for this. This is a very serious issue the world is currently and requires a proper cure. This paper studies on Computed tomography (CT) a heart diagnosis imaging technique which gives clear 3D image of any internal organ especially heart it checks the calcium, fat deposits in your arteries a deep learning techniques like convolutional neural networks (CNN), Visual Geometry Group - 16(VGG-16) which is typically a CNN model with deep layers, Visual Geometry Group - 19(VGG-19), Recurrent neural networks. The primary objective of this study is to create a highly accurate deep learning neural network model which takes images of CT scan by analysing the patterns in the image and tell us a person is having heart disease or not, This research made a comparative analysis of Both Machine learning models and deep learning models like Visual geometry group – 16(VGG-16) , Visual geometry group -19(VGG-19) and Recurrent Neural networks. It is observed that VGG-16 which is typically a CNN architecture has a performance similar to CNN Model, Whereas VGG-19 has showcased the best performance by giving highest accuracy and low false positive rate And Recurrent neural networks also performed well. This study is an example that deep learning models have better performance than ML models due to their ability to extract complex features.
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