Deep Learning-Based Diagnosis of Brain Cancer Using Convolutional Neural Networks On MRI Scans: A Comparative Study of Model Architectures and Tumor Classification Accuracy

Abstract

Brain tumor diagnosis using magnetic resonance imaging (MRI) is essential for timely intervention and treatment planning, yet manual interpretation is often time-consuming and subject to observer variability. Deep learning, particularly convolutional neural networks (CNNs), has shown considerable promise in automating tumor classification with high accuracy. This study developed and evaluated a LightBT-CNN model using the Brain Tumor MRI dataset, consisting of 7,023 images categorized into glioma, meningioma, pituitary tumor, and no tumor classes. The model was trained, validated, and tested using Python and TensorFlow, with performance evaluated through classification metrics and Gradient-weighted Class Activation Mapping (Grad-CAM) for interpretability. The LightBT-CNN model achieved an overall classification accuracy of 98% accuracy, with strong precision, recall, and F1-scores across tumor types. Grad-CAM visualizations confirmed that the model focused on tumor-specific regions, strengthened the reliability of predictions, and enhanced clinical interpretability.These findings align with existing research, such as Ait Amou and his colleagues (2022), who reported 98.70% accuracy with CNN and Bayesian optimization, Haq and his colleagues (2023), who achieved 99.89% with a multi-level CNN, and Sun (2021), whose customized CNN attained 96% accuracy with an AUC of 0.99. The results demonstrate the feasibility of integrating CNN-based approaches into brain tumor diagnostics, with explainable AI tools like Grad-CAM further supporting clinical accuracy and adoption.