Perspective Review on Deep Learning Models to Medical Image Segmentation - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2022

Perspective Review on Deep Learning Models to Medical Image Segmentation

Résumé

In recent days, deep learning is on rage and is gaining a huge amount of popularity due to its supremacy in terms of accuracy. Deep learning is being used for a vast number of applications out of which healthcare is an important category. In this paper, we discuss the role of deep learning in medical image segmentation. It is also known as the automated or semi-automated detection of edges within various medical image modalities so as to identify the region of interest. Furthermore, we also explore the various deep learning networks that are widely preferred for medical image segmentation along with the architecture and overview of each network. This paper covers the most recent and widely preferred deep learning networks such as Convolutional Neural Network (CNN) and other related networks such as Alexnet, Resnet, U-net and V-net. The challenges and limitations of the emerging DL networks is also studied.
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Dates et versions

hal-04381273 , version 1 (09-01-2024)

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H. Heartlin Maria, A. Maria Jossy, S. Malarvizhi. Perspective Review on Deep Learning Models to Medical Image Segmentation. 5th International Conference on Computational Intelligence in Data Science (ICCIDS), Mar 2022, Virtual, India. pp.184-206, ⟨10.1007/978-3-031-16364-7_15⟩. ⟨hal-04381273⟩
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