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

作　　者：Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas 

机构地区：[1]School of Computer Science and Engineering,Vellore Institute of Technology,Vellore,632014,India [2]Department of Computer Science and Information Engineering,Asia University,Taichung,41354,Taiwan [3]Department of Medical Research,China Medical University Hospital,China Medical University,Taichung,406,Taiwan [4]Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology,School of Mathematics and Big Data,Foshan University,Foshan,528000,China [5]Medica Superspecialty Hospital,Kolkata,700099,India [6]Automation and Applied Informatics,Aurel Vlaicu University of Arad,Arad,310130,Romania

出　　处：《Computer Modeling in Engineering & Sciences》2023年第7期241-255,共15页工程与科学中的计算机建模（英文）

摘　　要：Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor younger.There is a need to develop new technologies andmethods to deal with this disease.Many researchers have proposed image processing-based solutions for CADdiagnosis,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 ConvolutionNeuralNetworks(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.

关 键 词：ANGIOGRAM convolution neural network coronary artery disease diagnosis of CAD image segmentation 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]