A Review on Deep Learning Architecture and Methods for MRI Brain Tu- mour Segmentation

被引:22
作者
Angulakshmi, M. [1 ]
Deepa, M. [1 ]
机构
[1] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore, Tamil Nadu, India
关键词
Deep learning; MRI; brain tumour; classification; architecture; challenges; CONVOLUTIONAL NEURAL-NETWORKS; IMAGE SEGMENTATION; TUMOR DETECTION; FUSION; CLASSIFICATION; REPRESENTATION; MACHINE; MODEL; CNN;
D O I
10.2174/1573405616666210108122048
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background: The automatic segmentation of brain tumour from MRI medical images is mainly covered in this review. Recently, state-of-the-art performance is provided by deep learning-based approaches in the field of image classification, segmentation, object detection, and tracking tasks. Introduction: The core feature deep learning approach is the hierarchical representation of features from images, thus avoiding domain-specific handcrafted features. Methods: In this review paper, we have dealt with a review of Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation. First, we have discussed the basic architecture and approaches for deep learning methods. Secondly, we have discussed the literature survey of MRI brain tumour segmentation using deep learning methods and its multimodality fusion. Then, the advantages and disadvantages of each method are analyzed and finally, it is concluded with a discussion on the merits and challenges of deep learning techniques. Results: The review of brain tumour identification using deep learning. Conclusion: Techniques may help the researchers to have a better focus on it.
引用
收藏
页码:695 / 706
页数:12
相关论文
共 105 条
[1]   Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network [J].
Amin, Javaria ;
Sharif, Muhammad ;
Gul, Nadia ;
Yasmin, Mussarat ;
Shad, Shafqat Ali .
PATTERN RECOGNITION LETTERS, 2020, 129 :115-122
[2]   Walsh Hadamard kernel-based texture feature for multimodal MRI brain tumour segmentation [J].
Angulakshmi, M. ;
Priya, G. G. Lakshmi .
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2018, 28 (04) :254-266
[3]   Automated brain tumour segmentation techniques-A review [J].
Angulakshmi, M. ;
Lakshmi Priya, G. G. .
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2017, 27 (01) :66-77
[4]  
[Anonymous], 2016, P IEEE C COMP VIS PA
[5]  
[Anonymous], 2013, Advances in neural information processing systems
[6]  
Ayhan M, 2013, INT C MACH LEARN JUN
[7]   A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images [J].
Bajaj, Aaishwarya Sanjay ;
Chouhan, Usha .
CURRENT MEDICAL IMAGING, 2020, 16 (08) :937-945
[8]  
Briot JP, 1709 ARXIV
[9]  
Briot JP, 1712 ARXIV
[10]  
Brosch T, 2013, LECT NOTES COMPUT SC, V8150, P633, DOI 10.1007/978-3-642-40763-5_78