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3D Brain Tumor Segmentation with U-Net Network using Public Kaggle Dataset | IEEE Conference Publication | IEEE Xplore
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3D Brain Tumor Segmentation with U-Net Network using Public Kaggle Dataset

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S. Sujatha; T. Sreenivasulu Reddy
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Abstract:

This paper aims to implement and experiment with a deep learning model, U-Net, for effectively segmenting the 3D-brain tumor images. It helps to identify glioblastoma in ...Show More

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Abstract:

This paper aims to implement and experiment with a deep learning model, U-Net, for effectively segmenting the 3D-brain tumor images. It helps to identify glioblastoma in MRI brain images. Manual investigation of MRI images does not provide exact information about the abnormalities in the images. Thus, various existing methods have proposed medical image processing methods, which include custom methods, traditional image segmentation methods, and classifiers. However, those methods did not provide high accuracy in prediction with lesser complexity. Hence, this paper has aimed to implement U-Net architecture for segmenting and classifying MRI brain images. It helps in obtaining efficient performance regarding brain tumor segmentation, along with detailing the stage in which the diseased person currently has after segmentation. The proposed methodology has been experimented within MATLAB software, and the efficiency has been verified. From the experiment , it is identified that the proposed model provides better performance of 98% Accuracy, 95% Precision, and 96.4% Recall. Furthermore, the size of the tumor was successfully able to be intimated by outlining it, and the stage of the Brain Tumor (Gliomas) was also intimated with the warning message to any healthcare professional. Finally, the comparative study of existing methods using Morphological operations and our proposed 3D brain tumor segmentation methodology was made and presented.
Published in: 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)
Date of Conference: 02-04 February 2023
Date Added to IEEE Xplore: 27 March 2023
ISBN Information:
DOI: 10.1109/ICAIS56108.2023.10073895
Conference Location: Coimbatore, India
References is not available for this document.
Contents

I. Introduction

A tumor is the grouping of aberrant cells in the brain that shape a cluster [1]. The hard skeletal form called the skull protects our brain. There are more chances for issues like variations or disturbances in this skull area. When tumors start to grow inside the brain, pressure may develop in the skull area. In some cases, this can result in permanent brain damage and even death [3]. Malignant (cancerous) and noncancerous (non-cancerous) brain tumors are also possible (benign) in the human brain [2].

Select All
1.
M. L. Bondy et al., "Brain tumor epidemiology: consensus from the Brain Tumor Epidemiology Consortium", vol. 113, no. S7, pp. 1953-1968, 2008.
CrossRef Google Scholar
2.
R. L. Achey, H. Gittleman, J. Schroer, V. Khanna, C. Kruchko and J. S. J. N.-o. Barnholtz-Sloan, "Nonmalignant and malignant meningioma incidence and survival in the elderly 2005–2015 using the Central Brain Tumor Registry of the United States", vol. 21, no. 3, pp. 380-391, 2019.
CrossRef Google Scholar
3.
A. Goriely et al., "Mechanics of the brain: perspectives challenges and opportunities", vol. 14, no. 5, pp. 931-965, 2015.
CrossRef Google Scholar
4.
X. Jiang, R. Zhang and S. J. P. P. Nie, "Image segmentation based on level set method", vol. 33, pp. 840-845, 2012.
CrossRef Google Scholar
5.
M. Nazir, S. Shakil and K. J. C. M. I. Khurshid, "Role of deep learning in brain tumor detection and classification (2015 to 2020): A review", vol. 91, pp. 101940, 2021.
CrossRef Google Scholar
6.
S. Bakas et al., "Identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the BRATS challenge", 2018.
Google Scholar
7.
A. K. Jain, J. Mao and K. M. J. C. Mohiuddin, "Artificial neural networks: A tutorial", vol. 29, no. 3, pp. 31-44, 1996.
View Article
Google Scholar
8.
M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt and B. J. I. I. S. Scholkopf, "Support vector machines", vol. 13, no. 4, pp. 18-28, 1998.
View Article
Google Scholar
9.
S. Indolia, A. K. Goswami, S. P. Mishra and P. J. P. c. s. Asopa, "Conceptual understanding of convolutional neural network-a deep learning approach", vol. 132, pp. 679-688, 2018.
CrossRef Google Scholar
10.
S. A. A. Ismael, A. Mohammed and H. J. A. i. i. m. Hefny, "An enhanced deep learning approach for brain cancer MRI images classification using residual networks", vol. 102, pp. 101779, 2020.
CrossRef Google Scholar
11.
M. I. Sharif, J. P. Li, M. A. Khan and M. A. J. P. R. L. Saleem, "Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images", vol. 129, pp. 181-189, 2020.
CrossRef Google Scholar
12.
N. Sravanthi, N. Swetha, P. R. Devi, S. Rachana, S. Gothane and N. J. I. J. S. R. C. S. E. I. T. Sateesh, "Brain tumor detection using image processing", vol. 7, pp. 348-352, 2021.
CrossRef Google Scholar
13.
L. Sun, S. Zhang, H. Chen and L. J. F. i. n. Luo, "Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning", vol. 13, pp. 810, 2019.
CrossRef Google Scholar
14.
G. Latif, J. Alghazo, F. N. Sibai, D. A. Iskandar and A. H. J. C. m. i. Khan, "Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation", vol. 17, no. 8, pp. 917-930, 2021.
CrossRef Google Scholar
15.
A. Hamzenejad, S. J. Ghoushchi and V. J. B. R. I. Baradaran, "Clustering of brain tumor based on analysis of mri images using robust principal component analysis (robpca) algorithm", vol. 2021, 2021.
CrossRef Google Scholar
16.
R. Pitchai, P. Supraja, A. H. Victoria and M. J. N. P. L. Madhavi, "Brain tumor segmentation using deep learning and fuzzy k-means clustering for magnetic resonance images", vol. 53, no. 4, pp. 2519-2532, 2021.
CrossRef Google Scholar
17.
M. K. Islam, M. S. Ali, M. S. Miah, M. M. Rahman, M. S. Alam and M. A. J. M. L. w. A. Hossain, "Brain tumor detection in MR image using superpixels principal component analysis and template based K-means clustering algorithm", vol. 5, pp. 100044, 2021.
CrossRef Google Scholar
18.
E. S. Biratu, F. Schwenker, T. G. Debelee, S. R. Kebede, W. G. Negera and H. T. J. J. o. I. Molla, "Enhanced region growing for brain tumor MR image segmentation", vol. 7, no. 2, pp. 22, 2021.
CrossRef Google Scholar
19.
E. Irmak, "Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework", J Iranian Journal of Science Technology Transactions of Electrical Engineering, vol. 45, no. 3, pp. 1015-1036, 2021.
CrossRef Google Scholar
20.
S. Chatterjee, F. A. Nizamani, A. Nürnberger and O. J. S. R. Speck, "Classification of brain tumours in MR images using deep spatiospatial models", vol. 12, no. 1, pp. 1-11, 2022.
CrossRef Google Scholar
21.
J. Amin, M. Sharif, A. Haldorai, M. Yasmin and R. S. J. C. Nayak, "Brain tumor detection and classification using machine learning: a comprehensive survey", pp. 1-23, 2021.
CrossRef Google Scholar
22.
R. Dubey, M. Hanmandlu, S. Gupta and S. J. I. J. o. S. Gupta, "Region growing for MRI brain tumor volume analysis RB Dubey1 M. Hanmandlu2 SK Gupta3 and SK Gupta4", vol. 2, no. 9, 2009.
Google Scholar
23.
T. Kalaiselvi and P. J. I. j. o. i. s. Nagaraja, "A rapid automatic brain tumor detection method for MRI images using modified minimum error thresholding technique", vol. 1, no. 25, pp. 77-85, 2015.
Google Scholar
24.
J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Springer Science & Business Media, 2013.
CrossRef Google Scholar
25.
K. Somasundaram and T. Kalaiselvi, "A comparative study of segmentation techniques used for MR brain images", The International Conference on Image Processing Computer Vision and Pattern Recognition–IPCV, vol. 9, pp. 597-603, 2009.
Google Scholar
26.
N. Moon, E. Bullitt, K. v. Leemput and G. Gerig, "Automatic brain and tumor segmentation", International conference on medical image computing and computer-assisted intervention, pp. 372-379, 2002.
CrossRef Google Scholar
27.
J. Liu, J. K. Udupa, D. Odhner, D. Hackney and G. J. C. M. I. Moonis, "A system for brain tumor volume estimation via MR imaging and fuzzy connectedness", vol. 29, no. 1, pp. 21-34, 2005.
CrossRef Google Scholar
28.
Y. Lu, T. Jiang and Y. J. N. Zang, "Region growing method for the analysis of functional MRI data", vol. 20, no. 1, pp. 455-465, 2003.
CrossRef Google Scholar
29.
Y. Salman, M. Assal, A. Badawi, S. Alian and M. E.-M. El-Bayome, "Validation techniques for quantitative brain tumors measurements", 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 7048-7051, 2006.
View Article
Google Scholar
30.
Y. Yang, X. Yan, C. Zheng and P. Lin, "A novel statistical method for segmentation of brain MRI", 2004 International Conference on Communications Circuits and Systems (IEEE Cat. No. 04EX914), vol. 2, pp. 946-949, 2004.
View Article
Google Scholar

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