MRI-based brain tumor prediction using convolutional neural network framework

Deep learning technology enables the transformation of healthcare, focusing on the deployment of lightweight or hybrid diagnostic models for insufficiently resourced medical facilities. Early-stage brain tumor diagnosis supports doctors in developing better treatment strategies and enhancing prognosis results. In this work, we present a CNN-based model for automated brain tumor identification through magnetic resonance imaging (MRI). Our model achieved better generalization capability through pre-processed imaging that involved normalization followed by segmentation. Optimization was carried out using hyperparameter tuning, early stopping and model checkpointing. The model exhibited remarkable performance, attaining 99% accuracy, sensitivity and specificity and precision. Reliability and robustness measurements were validated through comprehensive evaluation metrics, including confusion matrix analysis, receiver operating characteristic (ROC) curve and F1-score. The findings indicate the effectiveness of the proposed CNN framework as an effective and computationally feasible decision-support system in aid of radiologists in the early detection of brain tumors, especially in the context of limited resources healthcare settings.