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Deep Neural Networks for Medical Image Segmentation - 2022

Deep Neural Networks For Medical Image Segmentation

Research Paper on Deep Neural Networks For Medical Image Segmentation

Research Area:  Machine Learning

Abstract:

Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of the organs, diseases, or abnormalities in medical images has become demanding. The segmentation of medical images helps in checking the growth of disease like tumour, controlling the dosage of medicine, and dosage of exposure to radiations. Medical image segmentation is really a challenging task due to the various artefacts present in the images. Recently, deep neural models have shown application in various image segmentation tasks. This significant growth is due to the achievements and high performance of the deep learning strategies. This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks. The paper examines the various widely used medical image datasets, the different metrics used for evaluating the segmentation tasks, and performances of different CNN based networks. In comparison to the existing review and survey papers, the present work also discusses the various challenges in the field of segmentation of medical images and different state-of-the-art solutions available in the literature.

Keywords:  
Deep Neural Networks
Medical Image Segmentation
Deep Learning
Machine Learning

Author(s) Name:  Priyanka Malhotra ,Sheifali Gupta ,Deepika Koundal ,Atef Zaguia and Wegayehu Enbeyle

Journal name:  Journal of Healthcare Engineering

Conferrence name:  

Publisher name:  Hindawi

DOI:  10.1155/2022/9580991

Volume Information: