Brain Tumor Detection and Multi Classification Using GNB-Based Machine Learning Approach

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Published: Nov 2, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10.8427
Keywords:
Machine Learning, GLCM, Gaussian Naive Bayes, Adaptive boosting, MRI

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Satish N. Gujar
Professor, Bhivarabai Sawant College of Engineering &Research, Narhe, Pune, Maharashtra, India
Jaimala Jha
Asst Prof., Madhav Institute of Technology and Science Gwalior, Madhya Pradesh, India
Sanjaykumar P. Pingat
Research Scholar, JJT University, Rajasthan, India
Rashmi Pandey
Asst Prof., Institute of Technology and Management, Gwalior, India
Atul Kumar
Professor, Dr. D. Y. Patil B-School, Pune, India
Ashish Gupta
Asst Prof., Nagaji Institute of Technology Management, Gwalior, India
Priya Pise
Professor, Indira college of Engineering Management, Pune, Maharashtra India

Abstract

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.

Article Details

How to Cite
Gujar, S. N. ., Jha, J. ., Pingat, S. P. ., Pandey, R. ., Kumar, A. ., Gupta, A. ., & Pise, P. . (2023). Brain Tumor Detection and Multi Classification Using GNB-Based Machine Learning Approach. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 08–16. https://doi.org/10.17762/ijritcc.v11i10.8427
Issue
Vol. 11 No. 10 (2023)
Section
Articles

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