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Aortic Valve Segmentation using Convolutional Neural Network with Skip Mechanism

H. M. Zabir Haque. Published in Artificial Intelligence.

Communications on Applied Electronics
Year of Publication: 2019
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: H. M. Zabir Haque
10.5120/cae2019652819

Zabir H M Haque. Aortic Valve Segmentation using Convolutional Neural Network with Skip Mechanism. Communications on Applied Electronics 7(29):1-5, June 2019. BibTeX

@article{10.5120/cae2019652819,
	author = {H. M. Zabir Haque},
	title = {Aortic Valve Segmentation using Convolutional Neural Network with Skip Mechanism},
	journal = {Communications on Applied Electronics},
	issue_date = {June 2019},
	volume = {7},
	number = {29},
	month = {Jun},
	year = {2019},
	issn = {2394-4714},
	pages = {1-5},
	numpages = {5},
	url = {http://www.caeaccess.org/archives/volume7/number29/853-2019652819},
	doi = {10.5120/cae2019652819},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Segmentation is a method which can be implemented inside the verge of Artificial Intelligence World. In this approach, each pixel of an image is required to be labeled to yield the final segmented result. In this paper, a novel method has been proposed which is conducted following by Convolutional Neural Network (CNN) with skip mechanisms for Segmentation. In this method, the original 3D medical image captured as a 2D slice to pass through multiple image channels along with Ground Truth in the last channel which work as an input of CNN. This sub-sample, however, gradually generate the segmentation mask for the corresponding input image. The proposed methods were tested to perform segmentation for the CT image of the human organ (Aortic Valve) which show a significant amount of accuracy with very few numbers of dataset. Here, the result has been compared with existing methods. Such a system, hence, will support many experiments to help better understanding of Humankind in the perspective of Artificial Visualization.

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Keywords

Segmentation, Convolutional Neural Network, Artificial Intelligence, Deep Learning, Medical Image, Human Organ, Aortic Valve, Image Channels, Ground Truth, Segmentation Mask