Enhancing Brain Tumor Classification: Ensemble Approaches with Prediction and Feature Fusions

Brain tumors are one of the most serious kinds of cancer that arise from the unchecked growth of brain cells inside the skull. Early and effective diagnosis of brain tumors is critical as it is a life-threatening illness. In recent decades, Deep Learning methodologies have gained significant traction for developing Computer-aided systems capable of swiftly and accurately diagnosing brain tumors. However, the primary challenge in leveraging robust deep learning models lies in identifying the most suitable classifier for a given classification task. This task is inherently demanding and time-consuming, involving the identification of optimal network architectures and carefully fine-tuning numerous hyper-parameters. To address these challenges, this paper proposes several ensemble methods aimed at fusing multiple baseline deep learning models in the domain of brain tumors. To accomplish this, baseline convolution-based models are trained, including the proposed CNN model. Various ensemble techniques, such as meta-learning, voting, and weighted voting, are applied. An ensemble method is also introduced based on the fusion of extracted baseline features. Experimental results and comparative analysis on a brain tumor dataset demonstrate that ensemble methods enhance the performance of the baseline model, yielding an accuracy score improvement of 16% and 1.3%.