The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network
Open accessAbstract
The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.
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Cites
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., & Ellison, D. W. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1545-1 · Cited by 15,657
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
(2010) · Cited by 14,134
DeAngelis, L. M. (2001) · New England Journal of Medicine · doi:10.1056/nejm200101113440207 · Cited by 1,663
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
Wong, T.-T. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.009 · Cited by 1,562
Deep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129
Classification using deep learning neural networks for brain tumors
Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2018) · Future Computing and Informatics Journal · doi:10.1016/j.fcij.2017.12.001 · Cited by 1,074
Multi-grade brain tumor classification using deep CNN with extensive data augmentation
Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., & Baik, S. W. (2019) · Journal of Computational Science · doi:10.1016/j.jocs.2018.12.003 · Cited by 891
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140381 · Cited by 879 · cited 1×
“In the literature, there are also studies that used the same database for classification with pre-trained networks [23,35–40] or, as input, they use only tumor region or some features that are extracted from the tumor region [7,21,23,41,42].”
BackgroundBrain tumor classification for MR images using transfer learning and fine-tuning
Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., & Lu, J. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.05.001 · Cited by 840
Cited by
Irmak, E. (2021) · Iranian Journal of Science and Technology, Transactions of Electrical Engineering · doi:10.1007/s40998-021-00426-9 · Cited by 477
Akter, A., Nosheen, N., Ahmed, S., Hossain, M., Yousuf, M. A., Almoyad, M. A. A., Hasan, K. F., & Moni, M. A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122347 · Cited by 264
Deep learning in selected cancers’ image analysis—a survey
Debelee, T. G., Kebede, S. R., Schwenker, F., & Shewarega, Z. M. (2020) · Journal of Imaging · doi:10.3390/jimaging6110121 · Cited by 70
Enhancing brain tumor detection in MRI images using YOLO-NeuroBoost model
Chen, A., Lin, D., & Gao, Q. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1445882 · Cited by 35
Metadata
- Authors
- Badža, M. M., & Barjaktarović, M. Č.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-03-15
- Volume
- 10
- Issue
- 6
- Pages
- 1999
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3011430986
- Cited by
- 686
- References
- 41
- FWCI
- 24.90
- Type
- journal article
- Language
- en
- Open access
- gold
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