Empirical Analysis of Traumatic Brain Imaging Using Brain MR Images
Vikas Narayan Nandgaonkar
Dept. of Computer Science, Himalayan University, Itanagar, AP, India.
Dr Kailash Jagannath Karande
Research Supervisor, Dept. of Computer Science, Himalayan University, Itanagar, AP, India.
45-48
Vol: 9, Issue: 4, 2019
Receiving Date:
2019-09-14
Acceptance Date:
2019-11-25
Publication Date:
2019-12-10
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Abstract
Lately, deep learning strategies contain demonstrated the greatest shows in best competitions coping with MR image segmentations, like the brain tumor segmentation problem. The latest solutions for MR image segmentations have mainly employed 3D procedures to reveal the object’s spatial framework in 3D space. In order to boost effectiveness, cast methods applying mixtures concerning diversely designed architectures possess typically come used for MR image segmentations. This paper presents the machine learning techniques for brain MRI analysis
Keywords:
brain MRI analysis; magnetic resonance imaging (MRI); machine learning
References
- Jothi, G. (2016), Hybrid Tolerance Rough Set–Firefly based supervised feature selection for MRI brain tumor image classification; Applied Soft Computing 46: 639-651.
- Salvatore, Christian, Petronilla Battista, and Isabella Castiglioni. (2016), Frontiers for the early diagnosis of AD by means of MRI brain imaging and support vector machines; Current Alzheimer Research 13.5: 509-533
- Khare, Sneha, Neelesh Gupta, and Vibhanshu Srivastava. (2014), Optimization technique, curve fitting and machine learning used to detect brain tumor in MRI; Proceedings of IEEE International Conference on Computer Communication and Systems ICCCS14. IEEE
- Preethi, G., and V. Sornagopal. (2014), MRI image classification using GLCM texture features; international conference on green computing communication and electrical engineering (ICGCCEE). IEEE.
- Ahmadvand, Ali, and Peyman Kabiri. (2016), Multispectral MRI image segmentation using Markov random field model. Signal, Image and Video Processing 10.2: 251-258
- Hiralal, Reshma, and Hema P. Menon. (2016), A survey of brain MRI image segmentation methods and the issues involved. The International Symposium on Intelligent Systems Technologies and Applications. Springer, Cham.
- Xia, Yong, Zexuan Ji, and Yanning Zhang. (2016), Brain MRI image segmentation based on learning local variational Gaussian mixture models. Neurocomputing 204: 189-197.
- Javadpour, Amir, and Alireza Mohammadi. (2016), Improving brain magnetic resonance image (MRI) segmentation via a novel algorithm based on genetic and regional growth; Journal of biomedical physics & engineering 6.2: 95.
- Wang, Yabiao, et al. (2016), MRI image segmentation by fully convolutional networks; IEEE International Conference on Mechatronics and Automation. IEEE.
- Liu, Jianwei, and Lei Guo. (2015), An improved K-means algorithm for brain MRI image segmentation.; 3rd International Conference on Mechatronics, Robotics and Automation. Atlantis Press.
- Singh, Amanpreet, Narina Thakur, and Aakanksha Sharma. (2016), A review of supervised machine learning algorithms; 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE.
- Bhavsar, Hetal, and Amit Ganatra. (2012), A comparative study of training algorithms for supervised machine learning; International Journal of Soft Computing and Engineering (IJSCE) 2.4: 2231-2307.
- Choudhary, Rishabh, and Hemant Kumar Gianey. (2017), Comprehensive review on supervised machine learning algorithms; International Conference on Machine Learning and Data Science (MLDS). IEEE.
- Jabbar, H., and Rafiqul Zaman Khan. (2015), Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study); Computer Science, Communication and Instrumentation Devices : 163-172.
- Ye, Qiang, Ziqiong Zhang, and Rob Law, (2009), Sentiment classification of online reviews to travel destinations by supervised machine learning approaches; Expert systems with applications 36.3: 6527-6535.
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