Multi-class classification of brain tumors using optimized CNN and transfer learning techniques

Key Points

• Classification accuracy reached 92% across diverse tumor types, indicating strong model performance.
• The evaluation includes datasets from multiple institutions to enhance generalizability.
• Assessment employed optimized convolutional neural networks with transfer learning techniques for efficiency.
• Implications include potential advancements in diagnostic processes, facilitating early and accurate tumor detection.