Revolutionizing Brain Tumor Classification with Fusion-Driven Deep Learning Models

The identification of brain tumors presents a significant medical challenge, as timely and precise diagnosis greatly influences patient survival rates. Brain tumors, caused by abnormal cell growth, vary in severity and often present subtle differences in medical imaging, making manual diagnosis by radiologists Labor-intensive and susceptible to mistakes. Magnetic resonance imaging (MRI) and Computed tomography (CT) scans plays a key role in enhancing tumor visibility and aiding diagnosis. However, the complexity of brain structures and the limitations of manual interpretation highlight the need for automated, efficient, and reliable diagnostic methods to support timely clinical decision-making. Multimodel approaches integrating information from various imaging techniques, including CT and MRI scans, have surfaced as a robust solution to enhance diagnostic accuracy. In this study, we propose a novel multimodel framework leveraging ResNet50, a deep convolutional neural network designed for the detection of brain tumors. Our approach integrates CT and MRI scan data, utilizing their complementary information to achieve superior performance. The proposed system incorporates a late fusion strategy, where features extracted from the CT and MRI scans by independent ResNet50 models are combined for final classification. The model underwent training and evaluation using a comprehensive dataset, achieving an accuracy of 97.5% in tumor detection, significantly outperforming single-modality approaches. This high accuracy emphasizes the potential of multimodel frameworks in enhancing the reliability of brain tumor diagnostics. Our research indicates that multimodal data integration, unified with the advanced feature extraction capabilities of ResNet50, can substantially strengthened the precision of automated brain tumor recognition systems. This research paves the way for more effective diagnostic tools within clinical contexts, it may significantly lower diagnostic error rates and lead to improved health outcomes.