Brain Tumor Detection In MRI Scans Using Hybrid AI Techniques

Better patient outcomes result from early diagnosis and treatment planning, which depends on the identification of brain tumors. By fusing deep learning, machine learning, and feature extraction approaches, this study explores the application of hybrid AI techniques for automated tumor diagnosis in MRI scans. The suggested method improves accuracy, robustness, and efficiency by combining conventional machine learning classifiers with convolutional neural networks (CNNs) for feature extraction. The results of the experiment demonstrate how well hybrid AI models distinguish between areas impacted by tumors and healthy tissues, which makes them an important tool for computer-aided diagnosis in medical imaging. In particular, this technique improves classification accuracy by combining Convolutional Neural Networks (CNNs) with Support Vector Machines (SVMs), and incorporates Generative Adversarial Networks (GANs) with Transfer Learning to refine feature representation in limited datasets. Experimental results indicate that these hybrid AI models achieve an average accuracy of 96.18%, demonstrating their potential for early brain cancer detection. The system enhances image quality, applies edge detection, extracts critical features, and leverages a hybrid AI-based training model, significantly improving diagnostic precision and surpassing previous methods.