International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Computer Science & Engineering | India | Volume 12 Issue 11, November 2023


XNet: X - Ray Image Segmentation

Kannagi Rajkhowa [2] | Puran Bhat [2] | Harsh Chaudhary | Gurleen Kaur [4]


Abstract: Image segmentation, or the process of removing physical structure from medical images, is a crucial component in the majority of medical imaging applications. The medical images are divided into bone, soft tissue, and exposed beam zones throughout this process. Currently, there are many distinct forms of picture segmentation that each have their own benefits and drawbacks. We are using deep learning methods to provide a complete solution that will produce accurate and reliable forecasts. Low - level medical facilities frequently lack the tools necessary to handle and read the greater amounts of X - ray images needed for neural network training. As a result, we have developed a dataset solution that is state - of - the - art. Convolutional neural network technology known as XNet is widely used for the practical segmentation of X - ray pictures into bone, soft tissue, and exposed beam regions. In order to solve the specified challenge, we are employing this technology to generate an efficient and precise output. The use of machine learning will have various benefits overall currently used conventional methods. Our suggested solution will replace existing approaches that rely largely on a complicated network of traditional image - processing technologies. The goal of this research is to provide an improved segmentation technique that is used to segment X - ray pictures using XNet.


Keywords: Deep Learning, XNET, Segmentation, X - Ray


Edition: Volume 12 Issue 11, November 2023,


Pages: 232 - 238


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