Brain Tumor Classification and Detection using Dynamic Machine Learning

Brain Tumor detection and classification system propose a novel approach by introducing a dynamic ensemble machine learning algorithm. Leveraging models like MobileNetV2, EfficientNet, ResNet50, Inception, and GoogLeNet, our approach surpasses static models in adaptability. Trained on diverse features and architectures, our method forms an adaptive ensemble that dynamically selects the most effective algorithm at runtime, optimizing classification accuracy. Through a comprehensive evaluation on a diverse dataset, our approach demonstrates superior performance compared to static ensemble models. Key aspects include model diversity, dynamic selection criteria, and insights into real-world diagnostic scenarios. This dynamic ensemble machine learning technique not only advances medical image analysis but also addresses the need for adaptive and efficient models in practical diagnostic applications. The results showcase the potential of our method to enhance the accuracy and reliability of brain tumor detection, creating a robust framework for dynamic machine learning applications, particularly in the context of medical imaging. With the final test accuracy of the ensemble model at 98.98%, researchers and practitioners can pave the way for transformative breakthroughs.