EBT Deep Net: Ensemble brain tumor Deep Net for multi-classification of brain tumor in MR images

The classification of brain tumors (BTs) is an expensive intricate challenge in the section of clinical image analysis. Radiologists were able to reliably detect tumors using machine learning (ML) algorithms without the need for extensive surgery. Hand-crafted features are necessary for the conventional ML classifiers, which take a lot of effort. Deep Learning (DL), on the other hand, has gained popularity recently for identification and categorization -based functionalities because of its effective feature extraction capabilities. Creating the most precise DL architecture for classifying BTs is a huge task. These challenges inspired us to develop a cutting -edge and highly accurate framework based on DL and computational methods that evolve to effectively create the hybrid framework automatically for categorizing three distinct types of brain cancers on a vast library of MR images. We present a unique EBT Deep Net DL model that uses a substantial convolutional neural network (CNN) technique for the classification of three distinct forms of brain cancers: gliomas, tumors in brain meningeal tissue, and tumors in pituitary glands. Pretrained deep CNN models like ResNet50V2, ResNet152, DenseNet201, MobileNetV3, and VGG19 were used to extract deep features. The efficacy of this paper is examined using classification accuracy. In comparison to the most recent classification findings from ResNet50V2, ResNet152, DenseNet201, MobileNetV3, and VGG19, the proposed methodology had the greatest accuracy. The models ' respective accuracy on a classification test is as follows: VGG19 scored 97.46 %, ResNet152 scored 98 %, DenseNet201 scored 95.44 %, ResNet50V2 scored 90.04 %, MobileNetV3 scored 97.15 %, and the ensemble model outperformed all of them with an accuracy of 98.8 %. The suggested model demonstrated its advantage over the currently used approaches for classifying BTs from MR images.