Title: Brain tumour detection and multi classification using GNB-based machine learning architecture

Authors: Satish N. Gujar; Ashish Gupta; Sanjay Kumar P. Pingat; Rashmi Pandey; Atul Kumar; Deepak Gupta; Priya Pise

Addresses: Department of Computer Engineering, TSSM'S Bhivarabai Sawant College of Engineering & Research, Pune, Maharashtra, 411041, India ' Department of Computer Science and Engineering, Nagaji Institute of Technology and Management, Gwalior, Madhya Pradesh, 474001, India ' Computer Engineering Department, Smt. Kashibai Navale College of Engineering, Pune, Maharashtra, 411041, India ' Department of Computer Science and Application, Institute of Technology and Management, Gwalior, Madhya Pradesh, 474001, India ' Dr. D. Y. Patil B-School, Pune, Maharashtra, 411033, India ' Department of Computer Science and Engineering, Institute of Technology and Management, Gwalior, Madhya Pradesh, 474001, India ' Department of AI & DS, Indira College of Engineering and Management, Pune, Maharashtra, 410 506, India

Abstract: Brain tumours are abnormal tissues with rapidly reproducing cells, posing significant challenges for identification and treatment. This study proposes a multimodal approach using machine learning and medical techniques for early diagnosis and segmentation of brain tumours. Noisy magnetic resonance imaging (MRI) are processed with a geometric mean to simplify noise removal. Fuzzy c-means algorithms segment the images, aiding in the detection of specific areas of interest. The grey-level co-occurrence matrix (GLCM) algorithm is used for dimension reduction and feature extraction. Various machine learning techniques, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Gaussian Naive Bayes (NB), and Adaptive Boosting, classify the images. Among these methods, Gaussian NB is particularly effective for identifying and classifying brain tumours. This approach leverages advanced AI and neural network techniques to enhance early diagnosis and improve treatment outcomes.

Keywords: machine learning; GLCM; grey-level co-occurrence matrix; Gaussian Naive Bayes; adaptive boosting; MRI; magnetic resonance imaging.

DOI: 10.1504/IJDATS.2025.144963

International Journal of Data Analysis Techniques and Strategies, 2025 Vol.17 No.1, pp.20 - 35

Received: 01 May 2023
Accepted: 07 May 2024
Published online: 14 Mar 2025 *

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