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

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.