The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Brain Tumor Classification Using Convolutional Neural Networks
Open accessAbstract
The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate…etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation (i.e) accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed by using Convolutional Neural Networks (CNN) classification. The deeper architecture design is performed by using small kernels. The weight of the neuron is given as small. Experimental results show that the CNN archives rate of 97.5% accuracy with low complexity and compared with the all other state of arts methods.
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Cites
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
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
ARPN Journal of Engineering and Applied Sciences
(2023) · ARPN Journal of Engineering and Applied Sciences · doi:10.59018/arpn · Cited by 2,147
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
International Journal of Computer Science and Mobile Computing
(2021) · International Journal of Computer Science and Mobile Computing · doi:10.47760/ijcsmc · Cited by 563
A survey of MRI-based brain tumor segmentation methods
Liu, J., Li, M., Wang, J., Wu, F., Liu, T., & Pan, Y. (2014) · Tsinghua Science and Technology · doi:10.1109/tst.2014.6961028 · Cited by 394
Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors
Islam, A., Reza, S. M. S., & Iftekharuddin, K. M. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2271383 · Cited by 319
Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications
Hamamci, A., Kucuk, N., Karaman, K., Engin, K., & Unal, G. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2181857 · Cited by 286
Brain Tumor Segmentation Based on Local Independent Projection-Based Classification
Huang, M., Yang, W., Wu, Y., Jiang, J., Chen, W., & Feng, Q. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2325410 · Cited by 219
Huda, S., Yearwood, J., Jelinek, H. F., Hassan, M. M., Fortino, G., & Buckland, M. (2016) · IEEE Access · doi:10.1109/access.2016.2647238 · Cited by 178
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
Abdelaziz Ismael, S. A., Mohammed, A., & Hefny, H. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101779 · Cited by 417
Role of deep learning in brain tumor detection and classification (2015 to 2020): A review
Nazir, M., Shakil, S., & Khurshid, K. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101940 · Cited by 293
Metadata
- Authors
- Seetha, J., & Raja, S. S.
- Venue
- Biomedical and Pharmacology Journal
- Published
- 2018-09-19
- Volume
- 11
- Issue
- 3
- Pages
- 1457-1461
- ISSN
- 0974-6242 (ISSN-L)
- 2456-2610
- OpenAlex
- W2889685514
- Cited by
- 425
- References
- 11
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- diamond
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