Advanced Deep Learning Architectures for Brain Tumor Detection: An Evaluation of EfficientNet, ResNet and Xception

Brain tumors, whether benign or malignant, pose significant challenges due to their impact on the brain and central nervous system, causing symptoms like headaches, seizures, and cognitive decline. Early detection is crucial, and advancements in MRI imaging, along with deep learning models such as EfficientNetB0, ResNet101, and Xception, have significantly improved diagnostic accuracy. EfficientNetB0, with an accuracy of 0.98, and both ResNet101 and Xception with 0.95, highlight the potential of these models to enhance brain tumor diagnosis and treatment. Integrating these models into clinical practice can improve diagnostic precision, streamline patient care, and enable timely interventions.