Accurate and timely diagnosis of brain tumors is essential for effective treatment, yet manual interpretation of MRI scans remains slow, subjective, and dependent on specialist expertise. This keynote presents an advanced deep learning framework designed to automate brain tumor classification with high reliability, consistency, and clinical relevance. The approach combines enhanced image preprocessing with multiple complementary deep learning models and an intelligent feature fusion strategy. By capturing diverse and discriminative patterns within MRI scans, the system delivers substantially improved classification performance compared to traditional singlemodel methods. In addition to identifying tumor types, the framework integrates automated tumor localization to provide valuable spatial insights for surgical planning and treatment evaluation. Beyond accuracy, the emphasis of this work is practicality: rapid processing, scalability, and seamless integration with existing clinical workflows. The system reduces diagnostic delays, minimizes interobserver variability, and enables specialist-level decision support in both advanced hospitals and resource-limited settings through telemedicine platforms. This keynote will discuss the vision, methodology, and translational potential of AI-driven brain tumor classification, highlighting key challenges in clinical deployment, the importance of trustworthy and explainable AI, and future directions such as multi-modal imaging and large-scale clinical validation. The goal is to show how intelligent, multi-model deep learning can help shape a more efficient, accessible, and data-driven future for neuroimaging and patient care. 

Muhammad Aamir received his Bachelor of Engineering degree in Computer Systems Engineering from Mehran University of Engineering & Technology, Jamshoro, Sindh, Pakistan, in 2008. He went on to earn a Master of Engineering degree in Software Engineering from Chongqing University, China, in 2014, and later completed his PhD in Computer Science and Technology from Sichuan University, Chengdu, China, in 2019. Currently, he is an Associate Professor in the College of Computer Science at Huanggang Normal University, China.

With extensive research experience and a prolific publication record, Dr. Aamir has made significant contributions to various fields including pattern recognition, computer vision, medical image processing, deep learning, and machine vision. His expertise and dedication to advancing these areas have earned him recognition as a peer reviewer for numerous prestigious journals and as an organizing committee member for several international conferences.