Authors: Devendra Gupta, Abhay Mundra
Abstract: Brain tumor detection from magnetic resonance imaging (MRI) is essential for early diagnosis, treatment planning, and improved patient outcomes. This study conducts a comparative evaluation of five deep learning approaches—CNN, ResNet50, U-Net, YOLOv7, and InceptionResNetV2—for automated brain tumor classification. Prior to model training, the dataset was prepared through systematic preprocessing, including data cleaning, normalization, and augmentation to improve robustness and reduce overfitting. Model performance was assessed using standard classification metrics: accuracy, precision, recall, and F1-score. Experimental results indicate that all evaluated architectures achieved strong predictive performance; however, InceptionResNetV2 consistently outperformed the other models, achieving near-perfect scores across all evaluation measures. This strong performance suggests improved reliability in reducing both false-positive and false-negative predictions, making InceptionResNetV2 a promising candidate for clinical decision-support applications. Overall, the findings highlight the importance of advanced deep learning architectures in delivering accurate and dependable MRI-based brain tumor detection.
