Development a Novel Hybrid Deep Learning-Model for Brain Tumor Classification and Automated Diagnosis

Abstract

Tumors of the brain are among the deadliest diseases of the century. Artificial intelligence (AI) and 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.