  To improve patient survival and treatment outcomes, early diagnosis of brain
tumors is an essential task. It is a difficult task to evaluate the magnetic
resonance imaging (MRI) images manually. Thus, there is a need for digital
methods for tumor diagnosis with better accuracy. However, it is still a very
challenging task in assessing their shape, volume, boundaries, tumor detection,
size, segmentation, and classification. In this proposed work, we propose a
hybrid ensemble method using Random Forest (RF), K-Nearest Neighbour, and
Decision Tree (DT) (KNN-RF-DT) based on Majority Voting Method. It aims to
calculate the area of the tumor region and classify brain tumors as benign and
malignant. In the beginning, segmentation is done by using Otsu's Threshold
method. Feature Extraction is done by using Stationary Wavelet Transform (SWT),
Principle Component Analysis (PCA), and Gray Level Co-occurrence Matrix (GLCM),
which gives thirteen features for classification. The classification is done by
hybrid ensemble classifier (KNN-RF-DT) based on the Majority Voting method.
Overall it aimed at improving the performance by traditional classifiers
instead of going to deep learning. Traditional classifiers have an advantage
over deep learning algorithms because they require small datasets for training
and have low computational time complexity, low cost to the users, and can be
easily adopted by less skilled people. Overall, our proposed method is tested
upon dataset of 2556 images, which are used in 85:15 for training and testing
respectively and gives good accuracy of 97.305%.
