Brain tumor identification with MRI imaging using convolutional neural networks

Globally, brain tumors are a pressing health concern due to their substantial contribution to cancer-related deaths. With brain cancer survival rates lingering at a low 35.7%, there is a critical demand for advancements in diagnostic methods and treatment strategies. Magnetic resonance imaging (MRI) plays a pivotal role in the detection and analysis of brain tumors, yet traditional manual interpretation of MRI scans is challenged by the complex morphological changes tumors introduce to brain structures. This study evaluates the efficacy of automated detection methods using advanced convolutional neural network (CNN) architectures. We specifically compare the performance of two CNN models, ResNet50 and MobileNetV2, on a dataset comprising 20,670 MRI images across four categories, including healthy brain scans. Our findings reveal that ResNet50 significantly surpasses MobileNetV2, achieving a validation accuracy of 99.31% and a test accuracy of 92.06%, compared to MobileNetV2’s validation accuracy of 80.7% and test accuracy of 82.0%. These results underscore ResNet50’s superior diagnostic capabilities, suggesting that the efficiency trade-offs associated with less complex models like MobileNetV2 are not justified in this scenario.