Brain tumor (BT) is considered as a major concern throughout the globe. The survivability of the patient is a challenging task if the BT is in a severe stage. It is very much essential for the early classification of BT into several categories so that preventive measures can be taken accordingly at the earliest. In this work, a fractal based machine intelligent (MI) based approach is proposed for the classification of brain tumor magnetic resonance imaging (MRI) images (BTMIs) into several categories such as glioma (GM), meningioma (MG), pituitary (PT) and notumor (NT)types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as AdaBoost (ADB), Random Forest (RFS), Decision Tree (DTR), KNearest Neighbor (KNNH), Support Vector Machine (SVMN), LRG and NNT for performance analysis. The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 400 BTMIs having 100 numbers of GM, MG, PT and NT type each are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as ADB, RFS, DTR, KNNH, SVMN, LRG and NNT

Brain Tumor MRI Images, Machine Intelligence, Classification Accuracy, F1, Precision, Recall