Deep Learning in Brain Tumor Recognition: “A Critical Review of Techniques, Limitations, and Future Prospects”

Deep learning has resulted in tremendous progress in the identification of brain tumors from Images of MRI and is the foundation for computer-aided diagnosis. Convolutional neural networks (CNN), transfer learning, and hybrid modelsare some of the techniques that have produced remarkable accuracy rates of up to 99.75 percent on ResNet, VGG, and EfficientNet designs. This work thoroughly examines the drawbacks of these methods, such as their dependence on large labeled datasets and susceptibility to overfitting. This work aims to improve the clinical usefulness and robustness of deep learning models in brain tumor diagnosis by identifying existing research gaps and proposing remedies.