Brain Disorder (BD) is considered as a major concern for the entire human society. It can destroy the thinking skills and memory of human beings. So, it is very much essential for the early classification of BD images (BDIs) 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 BDIs into several categories such as Atrofi (AF), Bci (BC), Iskemi (IK) and normal (NR) 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), K-Nearest 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, 444 BDIs having 100, 92, 102 and 150 numbers of AF, BC, IK and NR 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, RF, DTR, KNNH, SVMN, LRG and NNT

Brain Disorder Images, Machine Intelligence, Classification Accuracy, F1, Precision, Recall