Enhancing Brain Tumor Diagnosis with MedMamba: A Novel Application in Neural Imaging Analysis

Brain tumors pose a great threat to human health and need to be diagnosed at an early stage. MRI plays an important role in assisting diagnosis due to its clear soft tissue visualization. Currently, the application of deep learning techniques in medical imaging shows the possibility of outperforming humans in terms of diagnostic accuracy and speed. This study explores the application of transfer learning and MedMamba models for brain tumor diagnosis, with a particular focus on their role in performing MRI brain tumor image classification. The MedMamba model is introduced and evaluated, which is a new adaptation of the Mamba framework optimized for the medical imaging field. This study not only compares the performance of MedMamba models with traditional deep learning models such as Convolutional Neural Network and Vision Transformer, but also highlights the effectiveness of transfer learning in improving these models for specialized tasks in medical diagnosis. The main purpose is to describe the performance optimization capabilities provided by transfer learning and to demonstrate the inherent advantages of the MedMamba model. The model combines an innovative design with a streamlined architecture for efficient image processing. The positive role of transfer learning in the task of brain tumor image classification and the potential of MedMamba to set a new benchmark in this area are highlighted through comprehensive testing and evaluation of results on a brain MRI image dataset.