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In an abnormal tissue called a brain tumor, the cells of the tumor reproduce quickly. if no control over tumor cell growth. The difficulties involved in identifying and treating brain tumors Machine learning is the most technologically sophisticated tool for classification and detection, implementing reliable state-of-the-art A.I. as well as neural network classification techniques, the use of this technology in early diagnosis detection of brain tumors can be accomplished successfully. it is well known that the segmentation method is capable of helping simply destroy the brain's abnormal tumor regions In order to segment and categorize brain tumors, this study suggests a multimodal approach involving machine learning and medical assistance. Noise can be seen in MRI images. To make the method for eliminating noise from images easier, a geometric mean is used later. The algorithms used to segment an image into smaller pieces are fuzzy c-means algorithms. Detection of a specific area of interest is made simpler by segmentation. The dimension reduction procedure is carried out using the GLCM. Photographic features are extracted using the GLCM algorithm. Then, using a variety of ML techniques, like as CNN, ANN, SVM, Gaussian NB, and Adaptive Boosting, the photos are categorized. The Gaussian NB method performs more effectively with regard to the identification and classification of brain tumors. The plasterwork work achieved 98.80 percent accuracy using GNB, RBF SVM.
Malliga Subramanian, Jaehyuk Choet al, Multiple Types of Cancer Classification Using CT/MRI Images Based on Learning Without Forgetting Powered Deep Learning Models, IEEE Access, 30 January 2023,
R. Anjali and S. Priya, “An efficient classifier for brain tumor classification,” IJCSMC, vol. 6, no. 8, pp. 40–48, 2017
A. Raghuvanshi, U. K. Singh, and C. Joshi, “A review of various security and privacy innovations for IoT applications in healthcare,” Advanced Healthcare Systems, pp. 43–58, 2022.
V. D. P. Jasti, A. S. Zamani, K. Arumugam et al., “Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis,” Security and Communication Networks, vol. 2022, Article ID 1918379, 7 pages, 2022.
M. Angulakshmi and G. G. Lakshmi Priya, “Brain tumor segmentation from MRI using superpixels based spectral clustering,” Journal of King Saud University–Computer and Information Sciences, vol. 32, no. 10, pp. 1182–1193, 2018.
Badža MM, Barjaktarovi? M?. Classifcation of brain tumors from MRI images using a convolutional neural network. Appl Sci. 2020;10(6):1999.
S. R. Gunasekara, H. N. T. K. Kaldera, and M. B. Dissanayake, ‘‘A systematic approach for MRI brain tumor localization and segmentation using deep learning and active contouring,’’ J. Healthcare Eng., vol. 2021, pp. 1–13, Feb. 2021.
S.Rezayi, N. Mohammadzadeh, H. Bouraghi, S. Saeedi, and A. Mohammadpour, ‘‘Timely diagnosis of acute lymphoblastic leukemia using artificial intelligence-oriented deep learning methods,’’ Comput. Intell. Neurosci., vol. 2021, pp. 1–12, Nov. 2021.
M. Masud, N. Sikder, A.-A. Nahid, A. K. Bairagi, and M. A. AlZain, ‘‘A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework,’’ Sensors, vol. 21, no. 3, p. 748, Jan. 2021.
V. K. Reshma, N. Arya, S. S. Ahmad, I. Wattar, S. Mekala, S. Joshi, and D. Krah, ‘‘Detection of breast cancer using histopathological image classification dataset with deep learning techniques,’’ BioMed Res. Int., vol. 2022, pp. 1–13, Mar. 2022.
A. H. Khan, S. Abbas, M. A. Khan, U. Farooq, W. A. Khan, S. Y. Siddiqui, and A. Ahmad, ‘‘Intelligent model for brain tumor identification using deep learning,’’ Appl. Comput. Intell. Soft Comput., vol. 2022, pp. 1–10, Jan. 2022.
P. Mohanaiah, P. Sathyanarayana, and L. GuruKumar, “Image textural feature extraction using GLCM approach,” International Journal of Scientific and Research Publications, vol. 3, no. 5, pp. 1–5, 2013.
K. B. Vaishnavee and K. Amshakala, “An automated MRI brain image segmentation and tumor detection using SOMclustering and Proximal Support Vector Machine classifier,” in Proceedings of the 2015IEEE International Conference on Engineering and Technology (ICETECH), pp. 1–6, Coimbatore India, March 2015.
C. Hemalatha, S. Muruganand, and R. Maheswaran, “Preprocessing methods to remove impulse noise in avian pox affected Hen Image using Image Processing,” International Journal of Computer Application, vol. 98, no. 20, pp. 18–21, 2014.
M. Megha, A. Khare, and S. Jain, “A survey of digital image processing and its problem,” International Journal of Scientific and Research Publications, vol. 4, no. 2, 2014.
J. Rohini Paul, C. Senthil Singh, and M. Manikandan, “Brain tumor MRI image segmentation and detection in image processing,” International Journal of Renewable Energy Technology, vol. 3, no. 1, 2014.
Goyal, M.; Goyal, R.; Lall, B. Learning Activation Functions: A New Paradigm of Understanding Neural Networks. arXiv 2019, arXiv:1906.09529.
Jaeyong Kang, Zahid Ullah, Jeonghwan Gwak, “MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers, “Elsevier Sensors 2021,
M. Kalhor, A. Kajouei, F. Hamidi, and M. M. Asem, “Assessment of histogram-based medical image contrast enhancement techniques; an implementation,” in Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0997–1003, Las Vegas NV USA, January 2019.
M. Benco, P. Kamencay, M. Radilova, R. Hudec, and M. Sinko, “e comparison of color texture features extraction based on 1D GLCM with deep learning methods,” in Proceedings the International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 285–289, IEEE, Niteroi Brazil, July 2020.
T. R. Mahesh, V. Dhilip Kumar, V. Vinoth Kumar et al., “AdaBoost Ensemble methods using K-fold cross-validation for survivability with the early detection of heart disease,” Computational Intelligence and Neuroscience, vol. 2022, Article ID 9005278, 11 pages, 2022.
M. A. Khan and F. Algarni, “A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS,” IEEE Access, vol. 8, pp. 122259–122269, 2020.
E. F. Ohata, G. M. Bezerra, J. V. S. Chagas et al., “Automatic detection of COVID-19 infection using chest X-ray images through transfer learning,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 1, pp. 239–248, 2021.
Sartaj, “Brain tumor classification (MRI),” 2020, https://www. kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification mri.
M. G. Ertosun and D. L. Rubin, ‘‘Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks,’’ in Proc. AMIA Annu. Symp. Proc., vol. 2015, Nov. 2015, pp. 1899–1908.
E. I. Zacharaki, S. Wang, S. Chawla, D. S. Yoo, R. Wolf, E. R. Melhem, and C. Davatzikos, ‘‘Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme,’’ Magn. Reson. Med., vol. 62, no. 6, pp. 1609–1618, Dec. 2009
P. Afshar, K. N. Plataniotis, and A. Mohammadi, ‘‘Capsule networks for brain tumor classification based on MRI images and course tumor boundaries,’’ 2018, arXiv:1811.00597. [Online]. Available: https://arxiv.org/abs/1811.00597
J. S. Paul, A. J. Plassard, B. A. Landman, and D. Fabbri, ‘‘Deep learning for brain tumor classification,’’ Proc. SPIE, Med. Imag., Biomed. Appl. Mol., Struct., Funct. Imag., vol. 10137, Mar. 2017, Art. no. 1013710. doi: 10.1117/12.2254195.
A. K. Anaraki, M. Ayati, and F. Kazemi, ‘‘Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms,’’ Biocybernetics Biomed. Eng., vol. 39, no. 1, pp. 63–74, Jan./Mar. 2019.
Z. Huang, X. Du, L. Chen, Y. Li, M. Liu, Y. Chou, and L. Jin, ‘‘Convolutional neural network based on compl ex networks for brain tumor image classification with a modified activation function,’’ IEEE Access, vol. 8, pp. 89281–89290,2020,doi: 10.1109/ACCESS.2020.2993618.
A. Ari and D. Hanbay, ‘‘Deep learning based brain tumor classification and detection system,’’ TURKISH J. Electr. Eng. Comput. Sci., vol. 26, no. 5, pp. 2275–2286, Sep. 2018, doi: 10.3906/elk-1801-8.
E. S. A. El-Dahshan, T. Hosny, and A. B. M. Salem, ‘‘Hybrid intelligent techniques for MRI brain images classification,’’ Digit. Signal Process., vol. 20, pp. 433–441, Mar. 2010
Ashish Gupta, Sanjeev Kumar Gupta, Pritaj Yadav, Deepak Gupta “Different Technique for Detecting Plant Leaf Disease Using Machine Learning” Pub. By Springer by Scopes Index 3rd International Conference,24-25, February 2023.