A Survey on Medical Image Segmentation Based on Deep Learning Techniques

Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent years, and also discusses the fundamentals of...

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Bibliographic Details
Published in:Big data and cognitive computing Vol. 6; no. 4; p. 117
Main Authors: Moorthy, Jayashree, Gandhi, Usha Devi
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2022
Subjects:
Algorithms
applications
Artificial intelligence
Classification
Deep learning
frameworks
Image analysis
Image classification
Image enhancement
Image processing
Image segmentation
Machine learning
medical image analysis
Medical imaging
Medical imaging equipment
Medical research
Methods
Neural networks
Object recognition
Surveys
ISSN:2504-2289, 2504-2289
Online Access:Get full text
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Summary:Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent years, and also discusses the fundamentals of deep learning concepts applicable to medical image segmentation. The study of deep learning can be applied to image categorization, object recognition, segmentation, registration, and other tasks. First, the basic ideas of deep learning techniques, applications, and frameworks are introduced. Deep learning techniques that operate the ideal applications are briefly explained. This paper indicates that there is a previous experience with different techniques in the class of medical image segmentation. Deep learning has been designed to describe and respond to various challenges in the field of medical image analysis such as low accuracy of image classification, low segmentation resolution, and poor image enhancement. Aiming to solve these present issues and improve the evolution of medical image segmentation challenges, we provide suggestions for future research.
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ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc6040117