Brain Tumor Classification based on Convolutional Neural Networks with an Ensemble Learning Approach through Soft Voting

The brain is a vital organ that serves various purposes in the human body. Processing sensory data, generating muscle movements, and performing complex cognitive tasks have all historically relied heavily on the brain. One of the most common conditions affecting the brain is the growth of abnormal tissue in brain cells, leading to the development of brain tumors. The most common forms of brain tumors are pituitary, glioma, and meningioma, which are major global health issues. From these issues, there is a need for appropriate and prompt handling before the brain tumor disease becomes more severe. Quick handling is through an early disease detection approach, and computer vision is one of the trending early disease detection methods that can predict diseases using images. This research proposes a model in computer vision, namely the Convolutional Neural Network (CNN), with a soft voting ensemble learning strategy to classify brain tumors. The dataset consists of 7,023 images without tumors and MRI brain tumors such as glioma, meningioma, and pituitary with a resolution of 512x512 pixels. This experiment investigates classifier models such as VGG16, MobileNet, ResNet50, and DenseNet121, each of which has been optimized to maximize performance. The proposed soft voting ensemble strategy outperformed existing methods, with an accuracy of 97.67% and a Cohen's Kappa value of 0.9688. The proposed soft voting ensemble method approach has proven effective in improving the accuracy.