Brain tumors, if not detected in their early stages, pose a significant threat to human life. Current diagnostic practices heavily rely on the expertise and availability of radiologists, which can be inconsistent, time-consuming, and prone to error. This highlights an urgent need for advanced, automated solutions that deliver both accuracy and efficiency.

In this keynote, I will present a transformative approach to brain tumor detection using magnetic resonance imaging (MRI) and state-of-the-art deep learning methodologies. Our proposed technique begins by enhancing the visual quality of brain MRI images through preprocessing. We then leverage the strengths of two pre-trained deep learning models to extract robust features from these images. These feature sets are seamlessly integrated using the partial least squares (PLS) method to create a hybrid feature vector that ensures comprehensive representation.

Further, tumor localization is achieved via agglomerative clustering, identifying key regions of interest. These localized areas are standardized and fed into a head network for final classification, achieving an unprecedented accuracy of 98.95%—a notable improvement over existing methods.

This work exemplifies how interdisciplinary advancements in artificial intelligence and medical imaging can pave the way for more reliable, efficient, and accessible diagnostic solutions. The session will explore the implications of this research for future medical imaging technologies, bridging the gap between AI innovation and real-world healthcare impact.