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The architecture lacks a significant degree of semantics Nets and Overall Survival Prediction Using Radiomic
and local features between the pieces owed to the limitations Features” Front. Comput. Neurosci., vol. 14, no. April, pp.
of the 2D U-Net model in fully exploiting 3D data from MRI 1–12, 2020.
data. To enhance our effectiveness and demonstrate the
generalizability of the model by applying it to other datasets, [9] F. Isensee, P. F. Jäger, P. M. Full, P. Vollmuth, and K.
we want to examine a 3D network model in the future. H. Maier-Hein, “nnU-Net for Brain Tumor
Segmentation,” pp. 118–132, 2021.
CONFLICT OF INTEREST
[10] S. R. Gunasekara, H. N. T. K. Kaldera, and M. B.
The authors have no conflict of relevant interest to this Dissanayake, “A Systematic Approach for MRI Brain
article.
Tumor Localization and Segmentation Using Deep
REFERENCES Learning and Active Contouring” J. Healthc. Eng., vol.
[1] “Brain Tumor: Types, Risk Factors, and Symptoms.” 2021, 2021.
https://www.healthline.com/health/brain-tumor (accessed [11] O. Ronneberger, Philipp Fischer, and T. Brox, “U-Net:
May 25, 2021).
Convolutional Networks for Biomedical Image
[2] S. Puch, “Multimodal brain tumor segmentation in Segmentation” CoRR, vol. abs/1505.0, pp. 16591–16603,
Magnetic Resonance Images with Deep Architectures” no.
July, pp. 1–29, 2018. 2015.
[3] L. Cai, J. Gao, and D. Zhao, “A review of the [12] N. E. A. Khalid, M. F. Ismail, M. A. A. B. Manaf, A. F.
application of deep learning in medical image A. Fadzil, and S. Ibrahim, “MRI brain tumor
classification and segmentation” Ann. Transl. Med., vol. 8, segmentation: A forthright image processing approach”
no. 11, pp. 713–713, 2020. Bull. Electr. Eng. Informatics, vol. 9, no. 3, pp. 1024–
[4] H. Dong, G. Yang, F. Liu, Y. Mo, and Y. Guo, 1031, 2020.
“Automatic brain tumor detection and segmentation using [13] “Canny Edge Detection Step by Step in Python —
U-net based fully convolutional networks” Commun.
Comput. Inf. Sci., vol. 723, pp. 506–517, 2017. Computer Vision | by Sofiane Sahir | Towards Data
Science.” https://towardsdatascience.com/canny-edge-
[5] P. Chinmayi, L. Agilandeeswari, M. P. Kumar, and M.
K, “An Efficient Deep Learning Neural Network-based detection-step-by-step-in-python-computer-vision-
Brain Tumor Detection System” Intl. Jr. Pure Appl. Math.,
vol. 1, no. Special Issue, pp. 151–160, 2017. b49c3a2d8123 (accessed Nov. 12, 2021).
[14] “Intersection over Union (IoU) for object detection -
[6] S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain
Tumor Segmentation Using Convolutional Neural PyImageSearch.”
Networks in MRI Images” J. Med. Syst., vol. 43, no. 9, pp.
1240–1251, 2019. https://www.pyimagesearch.com/2016/11/07/intersection
[7] H. A. Khan, W. Jue, M. Mushtaq, and M. U. Mushtaq, -over-union-iou-for-object-detection/ (accessed Jul. 16,
“Brain tumor classification in MRI image using
convolutional neural network” Math. Biosci. Eng., vol. 17, 2021).
no. 5, pp. 6203–6216, 2020. [15] “F-Score Definition | DeepAI.”
[8] X. Feng, N. J. Tustison, S. H. Patel, and C. H. Meyer, https://deepai.org/machine-learning-glossary-and-terms/f-
“Brain Tumor Segmentation Using an Ensemble of 3D U-
score (accessed Jul. 16, 2021).
[16] “An overview of semantic image segmentation.”
https://www.jeremyjordan.me/semantic-segmentation/
(accessed Jul. 16, 2021).
[17] R. Ranjbarzadeh, A. Bagherian Kasgari, S. Jafarzadeh
Ghoushchi, S. Anari, M. Naseri, and M. Bendechache,
“Brain tumor segmentation based on deep learning and an
attention mechanism using MRI multi-modalities brain
images” Sci. Rep., vol. 11, no. 1, pp. 1–17, 2021.
