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Brain tumors (BT) are considered the second-principal cause of human death on our planet. They pose significant challenges in the field of medical diagnosis. Early detection is crucial for effective treatment and improved patient outcomes. As a result, researchers’ studies that deal with tumor detection play a vital role in early disease prediction in the field of medicine. Despite advancements in medical imaging technologies, accurate and efficient classification of BT remains a complex task. This study aims to address this challenge by proposing a novel method for brain tumor classification utilizing ensemble learning techniques combined with feature extraction from neuroimaging data. In the present paper, we present a novel approach for brain tumor classification that contains ensemble learning methods following the extraction of important features from brain tumor images. Our methodology involves the preprocessing of neuroimaging data, followed by feature extraction using descriptor techniques. These extracted features are then utilized as inputs to ensemble learning classifiers. Experimental results demonstrate the efficacy of the proposed approach in accurately classifying brain tumors with high precision and recall rates. The ensemble learning framework, combined with feature extraction, outperforms several benchmark models and methods commonly used in brain tumor classification, including AlexNet, VGG-16, and MobileNet, in terms of classification accuracy and computational efficiency. The proposed method that integrates ensemble learning techniques with feature extraction from neuroimaging data offers a promising solution for improving the accuracy and efficiency of brain tumor diagnosis, thereby facilitating timely intervention and treatment planning. The findings of this study contribute to the advancement of medical imaging-based classification systems for brain tumors, with implications for enhancing patient care and clinical decision-making in neuro-oncology.
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