BrainTumor CNN: brain tumor classification via transfer learning and ResNet-18

Abstract

Brain tumors affect millions of patients worldwide. Magnetic resonance imaging (MRI) is the preferred detection modality due to its radiation-free nature, high soft-tissue contrast, and three-dimensional (3D) imaging capabilities. However, the 3D complexity of MRI scans makes manual classification time-consuming, inefficient, and prone to errors. Consequently, developing high-precision automated classification is crucial in neurology. This study proposes BrainTumor CNN, a convolutional neural network (CNN) for classifying brain tumor MRI images. It leverages transfer learning via a pre-trained ResNet-18 network, integrating data augmentation and Dropout regularization to enhance robustness and generalization. The model demonstrates exceptional diagnostic performance, achieving 98.6% accuracy, with precision, recall, and F1-score all reaching 99.8%. By balancing high accuracy with low computational cost, this study provides an efficient diagnostic tool suitable for real-time clinical deployment.