PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Al-Dmour, H., & Al-Ani, A. (2018). A clustering fusion technique for MR brain tissue segmentation. Neurocomputing, 275, 546-559. https://doi.org/10.1016/j.neucom.2017.08.051

• Anaraki, A. K., Ayati, M., & Kazemi, F. (2019). Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybernetics and Biomedical Engineering, 39(1), 63-74. https://doi.org/10.1016/j.bbe.2018.10.004

• Asliyan, R., & Atbakan, İ. (2020). Automatic brain tumor segmentation with K-means, fuzzy c-means, self-organizing map and otsu methods. Selçuk-Teknik Dergisi, 19(4), 267-281.

• Baalamurugan, K. M., Singh, P., & Ramalingam, V. (2021). A novel approach for brain tumor detection by self-organizing map (SOM) using adaptive network based fuzzy inference system (ANFIS) for robotic systems. International Journal of Intelligent Unmanned Systems, 10(1), 98-116. https://doi.org/10.1108/IJIUS-08-2020-0038

• Baid, U., Talbar, S., & Talbar, S. (2017). Comparative study of k-means, gaussian mixture model, fuzzy c-means algorithms for brain tumor segmentation. Advances in Intelligent Systems Research, 137, 592-597.

• Ejaz, K., Rahim, M. S. M., Bajwa, U. I., Chaudhry, H., Rehman, A., & Ejaz, F. (2020). Hybrid segmentation method with confidence region detection for tumor identification. IEEE Access, 9, 35256-35278. https://doi.org/10.1109/ACCESS.2020.3016627

• Garcia-Lamont, F., Cervantes, J., López, A., & Rodriguez, L. (2018). Segmentation of images by color features: A survey. Neurocomputing, 292, 1-27. https://doi.org/10.1016/j.neucom.2018.01.091

• Guan, X., Yang, G., Ye, J., Yang, W., Xu, X., Jiang, W., & Lai, X. (2021). 3D AGSE-VNet: An automatic brain tumor MRI data segmentation framework. ArXiv e-prints.

• Helmy, A. K., & El-Taweel, G. S. (2016). Image segmentation scheme based on SOM–PCNN in frequency domain. Applied Soft Computing, 40, 405-415. https://doi.org.10.1016/j.asoc.2015.11.042

• Huang, H., Yang, G., Zhang, W., Xu, X., Yang, W., Jiang, W., & Lai, X. (2021). A deep multi-task learning framework for brain tumor segmentation. Frontiers in Oncology, 11, Article 690244. https://doi.org/10.3389/fonc.2021.690244

• Krishnakumar, S., & Manivannan, K. (2021). Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6751-6760.

• Kumar, K. A., Kumar, B. M., Veeramuthu, A., & Mynavathi, V. S. (2019). Unsupervised machine learning for clustering the infected leaves based on the leaf-colors. In D. K. Mishra, X. S. Yang & A. Unal (Eds.), Data Science and Big Data Analytics (pp. 303-312). Springer.

• Kumar, S. A., Harish, B. S., & Shivakumara, P. (2018). A novel fuzzy clustering based system for medical image segmentation. International Journal of Computational Intelligence Studies, 7(1), 33-66.

• Mohan, G., & Subashini, M. M. (2018). MRI based medical image analysis: Survey on brain tumor grade classification. Biomedical Signal Processing and Control, 39, 139-161.

• Mohan, G., & Subashini, M. M. (2019). Medical imaging with intelligent systems: A review. In A. K. Sangaiah (Ed.), Deep Learning and Parallel Computing Environment for Bioengineering Systems (pp. 53-73). Academic Press.

• Osman, A. H., & Alzahrani, A. A. (2018). New approach for automated epileptic disease diagnosis using an integrated self-organization map and radial basis function neural network algorithm. IEEE Access, 7, 4741-4747.

• Ren, T., Wang, H., Feng, H., Xu, C., Liu, G., & Ding, P. (2019). Study on the improved fuzzy clustering algorithm and its application in brain image segmentation. Applied Soft Computing, 81, Article 105503. https://doi.org/10.1016/j.asoc.2019.105503

• Sandhya, G., Kande, G. B., & Satya, S. T. (2019). An efficient MRI brain tumor segmentation by the fusion of active contour model and self-organizing-map. Journal of Biomimetics, Biomaterials and Biomedical Engineering, 40, 79-91. https://doi.org/10.4028/www.scientific.net/JBBBE.40.79

• Sandhya, G., Kande, G. B., & Savithri, T. S. (2020). Tumor segmentation by a self-organizing-map based active contour model (SOMACM) from the brain MRIs. IETE Journal of Research, 1-13. https://doi.org/10.1080/03772063.2020.1782780

• Sheela, C., & Suganthi, G. J. M. T. (2020). Morphological edge detection and brain tumor segmentation in magnetic resonance (MR) images based on region growing and performance evaluation of modified fuzzy C-means (FCM) algorithm. Multimedia Tools and Applications, 79(25), 17483-17496. https://doi.org/10.1007/s11042-020-08636-9

• Şişik, F., & Eser, S. E. R. T. (2020). Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware. Medical Hypotheses, 136, Article 109507. https://doi.org/10.1016/j.mehy.2019.109507

• Soltaninejad, M., Yang, G., Lambrou, T., Allinson, N., Jones, T. L., Barrick, T. R., Howe, F. A., & Ye, X. (2018). Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Computer Methods and Programs in Biomedicine, 157, 69-84. https://doi.org/10.1016/j.cmpb.2018.01.003

• Song, Q., Wu, C., Tian, X., Song, Y., & Guo, X. (2022). A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation. Applied Intelligence, 52(6), 6376-6397. https://doi.org/10.1007/s10489-021-02722-7

• Tarhini, G. M., & Shbib, R. (2020). Detection of brain tumor in MRI images using watershed and threshold-based segmentation. International Journal of Signal Processing Systems, 8(1), 19-25. https://doi.org/10.18178/ijsps.8.1.19-25

• Vijh, S., Sharma, S., & Gaurav, P. (2020). Brain tumor segmentation using OTSU embedded adaptive particle swarm optimization method and convolutional neural network. In J. Hemanth, M. Bhatia & O. Geman (Eds.), Data Visualization and Knowledge Engineering (pp. 171-194). Springer. https://doi.org/10.1007/978-3-030-25797-2_8

• Vishnuvarthanan, A., Rajasekaran, M. P., Govindaraj, V., Zhang, Y., & Thiyagarajan, A. (2017). An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images. Applied Soft Computing, 57, 399-426. https://doi.org/10.1016/j.asoc.2017.04.023

• Zhang, W., Yang, G., Huang, H., Yang, W., Xu, X., Liu, Y., & Lai, X. (2021). ME‐Net: multi‐encoder net framework for brain tumor segmentation. International Journal of Imaging Systems and Technology, 31(4), 1834-1848. https://doi.org/10.1002/ima.22571

• Zhang, Z., & Sejdić, E. (2019). Radiological images and machine learning: Trends, perspectives, and prospects. Computers in Biology and Medicine, 108, 354-370. https://doi.org/10.1016/j.compbiomed.2019.02.017