Brain Tumor Disease Classification Using Weighted Average Ensemble Deep Learning Model

In the context of medical research, brain tumors present a challenging problem due to their potential for rapid growth and their impact on overall health. Classifying brain tumors is an essential task for evaluating the tumors and selecting a course of treatment under their classes. In this study, we propose a weighted average ensemble technique for brain tumor classification. The proposed method combines the classification abilities of three pre-trained models, namely InceptionV3, Vgg16, and DenseNet121. We evaluate in detail how well each of these separate models performs in automatically classifying brain tumor MRI images. We then integrate their outputs using an ensemble learning approach by giving the appropriate weight for each base model. Our findings indicate a superior accuracy rate of 99.43% on the test dataset, surpassing the accuracy achieved by each base model.