Evaluating a Novel Hybrid Deep Learning Model for Brain Tumor Diagnosis

Tumors of the brain are among the deadliest diseases of the century. Artificial intelligence (ai) and<br>neuroscience are used to identify, identify, and categorize brain tumors. A large amount of mri scans must be manually segmented and categorized. An effective computer-aided diagnosis (cad) system is essential for the timely analysis of brain tumors. Magnetic resonance imaging (mri) is used to segment, extract, and classify brain tumors. All systems have achieved a superior level of performance. We used deep learning-based models to predict brain tumor disease with 99.10% accuracy. By analyzing huge amounts of data and extracting significant features accurately and efficiently, these models can offer improved patient outcomes and timely treatment in the healthcare sector in a short timeframe compared to existing approaches.