Optimized Deep Learning Model for Predicting Brain Tumors
Keywords:
Brain tumor prediction, deep CNN, WHT, MRI image, segmentationAbstract
Brain tumors, which grow unexpectedly and can cause brain damage, are diagnosed using magnetic resonance imaging (MRI) methods. However, these methods can be time-consuming and unreliable. deep learning models have been developed to predict brain cancers using MRI scans. The research developed a white headed timber optimization based deep CNN model, which uses the Brats 2019 and 2020 datasets as input. The model passes through pre-processing, segmentation, feature extraction, and fine-tuning using white-headed-timber based optimization. the training percentage (TP) and k-fold were consider using database 1, the accuracy (acc), sensitivity (sen), and specificity (spe) for D1 and D2 were achieved at 98.37%, 98.31%, and 98.88% correspondingly. For d2, the values were 97.74%, 98.53%, and 99.15%. Similar results were obtained during k-fold 10 for d1 98.37%, 98.31%, and 98.88%. d2 are 97.74%, 98.53%, and 99.15%. The research aims to develop a WHT-based deep CNN for predicting brain cancers using MRI scans.
Downloads
References
Irmak, E., “Multi-classification of brain tumor MRI images using deep convolution neural network with the fully optimized framework,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol.45,no.3, pp.1015-1036, 2021. Available from: https://link.springer.com/article/10.1007/s40998-021-00426-9
Hashemzehi, R., Mahdavi, S.J.S., Kheirabadi, M. and Kamel, S.R., “Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE,”biocybernetics and biomedical engineering,vol. 40,no.3, pp.1225-1232, 2020. Available from: http://dx.doi.org/10.1016/j.bbe.2020.06.001
Sharif, M.I., Li, J.P., Khan, M.A. and Saleem, M.A., “Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images,” Pattern Recognition Letters, vol.129, pp.181-189, 2020. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0167865519303411
Sharif, M.I., Li, J.P., Amin, J. and Sharif, A., “An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolution neural network,” Complex & Intelligent Systems, vol.7,no.4, pp.2023-2036, 2021. Available from: https://link.springer.com/article/10.1007/s40747-021-00310-3
Sharif, M., Amin, J., Raza, M., Yasmin, M. and Satapathy, S.C., “An integrated design of particle swarm optimization (PSO) with a fusion of features for detection of brain tumor,” Pattern Recognition Letters, vol.129, pp.150-157, 2020. Available from: http://dx.doi.org/10.1016/j.patrec.2019.11.017
Rammurthy, D. and Mahesh, P.K., “Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images,” Journal of King Saud University-Computer and Information Sciences, 2020. Available from: http://dx.doi.org/10.1016/j.jksuci.2020.08.006
Bhargavi, K. and Mani, J.J., “Early Detection of Brain Tumor and Classification of MRI Images Using Convolution Neural Networks,” Springer, Singapore, In Innovations in Computer Science and Engineering, pp. 427-436, 2019. Available from: https://link.springer.com/chapter/10.1007/978-981-13-7082-3_49
Ferahtia, S., Rezk, H., Abdelkareem, M.A. and Olabi, A.G., “Optimal techno-economic energy management strategy for building’s microgrids based bald eagle search optimization algorithm,” Applied Energy, vol.306, pp.118069, 2022. Available from: https://doi.org/10.1016/j.energy.2022.123447
Mirjalili, S., Mirjalili, S.M. and Lewis, A., “Grey wolf optimizer. Advances in engineering software,”vol.69, pp.46-61, 2014. Available from: http://dx.doi.org/10.1016/j.advengsoft.2013.12.007
Gower, Robert Mansel, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, and Peter Richtárik. "SGD: General analysis and improved rates." In International conference on machine learning, pp. 5200-5209. PMLR, 2019. Available from: https://doi.org/10.48550/arXiv.1901.09401
Aziz, Rabia Musheer, Mohammed Farhan Baluch, Sarthak Patel, and Abdul Hamid Ganie. "LGBM: a machine learning approach for Ethereum fraud detection." International Journal of Information Technology 14, no. 7 (2022): 3321-3331. Available from: http://dx.doi.org/10.1007/s41870-022-00864-6
Yang, Ying. "Medical multimedia big data analysis modeling based on DBN algorithm." IEEE Access 8 (2020): 16350-16361. Available from: https://doi.org/10.1109/ACCESS.2020.2967075
Mittal, Sparsh. "A survey on modeling and improving reliability of DNN algorithms and accelerators." Journal of Systems Architecture 104 (2020): 101689. Available from: https://doi.org/10.1016/j.sysarc.2019.101689
Sayed, Gehad Ismail, Mona M. Soliman, and Aboul Ella Hassanien. "A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization." Computers in biology and medicine 136 (2021): 104712. Available from: https://doi.org/10.1016/j.compbiomed.2021.104712
Kumar, Abhishek, SwarnAvinash Kumar, Vishal Dutt, Ashutosh Kumar Dubey, and Vicente García-Díaz. "IoT-based ECG monitoring for arrhythmia classification using Coyote Grey Wolf optimization-based deep learning CNN classifier." Biomedical Signal Processing and Control 76 (2022): 103638. Available from: https://doi.org/10.1016/j.bspc.2022.103638
BRATS 2019 dataset Available from: https://www.kaggle.com/datasets/aryashah2k/brain-tumor-segmentation-brats-2019
BRATS 2020 dataset Available from: https://www.kaggle.com/datasets/awsaf49/brats2020-training-data.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
