The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Brain tumor segmentation in multi‐spectral MRI using convolutional neural networks (CNN)
Abstract
A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research.
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Cites
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations
Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., & Jorge Cardoso, M. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-67558-9_28 · Cited by 2,829
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735
Interactive medical image segmentation using deep learning with image-specific fine tuning
Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791721 · Cited by 843
Cited by
Classification of acute lymphoblastic leukemia using deep learning
Rehman, A., Abbas, N., Saba, T., Rahman, S. I. U., Mehmood, Z., & Kolivand, H. (2018) · Microscopy Research and Technique · doi:10.1002/jemt.23139 · Cited by 328
Self-citationFeng, X., Tustison, N. J., Patel, S. H., & Meyer, C. H. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00025 · Cited by 267
Siva Raja, P. M., & rani, A. V. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2020.01.006 · Cited by 253
Grøvik, E., Yi, D., Iv, M., Tong, E., Nilsen, L. B., Latysheva, A., Saxhaug, C., Jacobsen, K. D., Helland, Å., Emblem, K. E., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00398-4 · Cited by 51
Metadata
- Authors
- Iqbal, S., Ghani, M. U., Saba, T., & Rehman, A.
- Venue
- Microscopy Research and Technique
- Publisher
- Wiley
- Published
- 2018-01-22
- Volume
- 81
- Issue
- 4
- Pages
- 419-427
- ISSN
- 1059-910X (ISSN-L)
- 1097-0029
- OpenAlex
- W2791575870
- Cited by
- 240
- References
- 5
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- closed
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