Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

Authors:
  • SRSanjiban Sekhar Roy
    Sanjiban Sekhar Roy
  • VBValentina E. Balas
    Valentina E. Balas
Open Access
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Abstract
Coronary artery disease (CAD) has become a significant cause of heart attack, especially among those 40 years old or younger. There is a need to develop new technologies and methods to deal with this disease. Many researchers have proposed image processing-based solutions for CAD diagnosis, but achieving highly accurate results for angiogram segmentation is still a challenge. Several different types of angiograms are adopted for CAD diagnosis. This paper proposes an approach for image segmentation using Convolution Neural Networks (CNN) for diagnosing coronary artery disease to achieve state-of-the-art results. We have collected the 2D X-ray images from the hospital, and the proposed model has been applied to them. Image augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s size. Also, the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high accuracy. As the output, different settings of the network architecture undoubtedly have achieved different accuracy, among which the highest accuracy of the model is 97.61%. Compared with the other models, these results have proven to be superior to this proposed method in achieving state-of-the-art results.

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