Early Detection of Brain Tumor from MRI Images Using Different Machine Learning Techniques

Abstract

Effective treatment of brain tumours depends on early identification of tumour tissues. Categorizing tumors is essential for its early identification. In order to split and categorise brain tumours using MRI images, various machine learning algorithms are examined. Preprocessing methods including image augmentation, rotation flipping, and adaptive deformation are used on the BRATs dataset to improve the data quality. Convolutional neural network (CNN) feature extraction techniques like VGG19 and InceptionV3 are applied. K-Nearest Neighbours (KNN) and logistic regression techniques are used to categorise the features. The proposed models’ accuracy for Logistic Regression, KNN, VGG19, Inception V3 and VGG19 with augmentation is 84.66%, 90.68%, 92.17%, 91.56% and 95.43% respectively. These findings imply that CNN-based feature extraction and picture augmentation techniques greatly improves the accuracy of brain tumour identification, Overall thecInception V3 with augmentation produces the best results.