Brain tumors classification using deep models and transfer learning

Abstract

Accurate brain tumor classification using magnetic resonance imaging (MRI) is crucial for guiding patient treatment decisions. However, differentiating tumor types can be challenging due to subtle variations in texture. This study investigates the potential of deep learning, specifically a 50-layer ResNet architecture, for improved brain tumor classification from MRI scans. The transfer learning technique is leveraged to enhance model performance and compare its effectiveness with other deep learning architectures such as CNN, RNN, and a dictionary learning-based classifier. The results demonstrate that the ResNet-50 model achieves superior performance in terms of accuracy, sensitivity, and robustness compared to the evaluated methods. This highlights the novelty of our work: combining a deep residual network (ResNet-50) with transfer learning for brain tumor classification. This approach offers a promising avenue for improved diagnostic accuracy and potentially better patient outcomes in a clinical setting with an accuracy rate of over 99.85%. The results of the experiments show that the proposed approach has significant potential in improving the accuracy of brain tumor classification using MRI and medical knowledge. Additionally, the use of deep learning structures combined with transfer learning yields a novel and effective solution for brain tumor classification.