Incorporating Grey Wolf Optimization and Recurrent Neural Networks for Accurate Brain Tumor Detection

This study presents a novel methodology for brain tumor detection using a combination of advanced image processing techniques and the RXS-Net model, which integrates ResNet-50, Xception, and SVM. The method involves preprocessing steps such as image resizing, greyscale conversion, anisotropic diffusion filtering, and contrast enhancement. The proposed method achieves an accuracy of 98.93%, significantly outperforming other techniques. This high level of accuracy, along with the model's robustness, underscores its potential for improving diagnostic accuracy in medical imaging.