MobileDenseAttn:A Dual-Stream Architecture for Accurate and Interpretable Brain Tumor Detection

TL;DR

MobileDenseAttn is a dual-stream neural network that improves brain tumor detection accuracy, efficiency, and interpretability in MRI scans, demonstrating superior performance and clearer tumor localization compared to baseline models.

Contribution

Introduces MobileDenseAttn, a fusion of MobileNetV2 and DenseNet201, enhancing feature representation, efficiency, and interpretability for brain tumor detection in MRI.

Findings

Achieves 98.35% testing accuracy

Reduces training time by 39.3% compared to VGG19

Provides clear tumor localization via GradCAM

Abstract

The detection of brain tumor in MRI is an important aspect of ensuring timely diagnostics and treatment; however, manual analysis is commonly long and error-prone. Current approaches are not universal because they have limited generalization to heterogeneous tumors, are computationally inefficient, are not interpretable, and lack transparency, thus limiting trustworthiness. To overcome these issues, we introduce MobileDenseAttn, a fusion model of dual streams of MobileNetV2 and DenseNet201 that can help gradually improve the feature representation scale, computing efficiency, and visual explanations via GradCAM. Our model uses feature level fusion and is trained on an augmented dataset of 6,020 MRI scans representing glioma, meningioma, pituitary tumors, and normal samples. Measured under strict 5-fold cross-validation protocols, MobileDenseAttn provides a training accuracy of 99.75%, a testing accuracy of 98.35%, and a stable F1 score of 0.9835 (95% CI: 0.9743 to 0.9920). The extensive validation shows the stability of the model, and the comparative analysis proves that it is a great advancement over the baseline models (VGG19, DenseNet201, MobileNetV2) with a +3.67% accuracy increase and a 39.3% decrease in training time compared to VGG19. The GradCAM heatmaps clearly show tumor-affected areas, offering clinically significant localization and improving interpretability. These findings position MobileDenseAttn as an efficient, high performance, interpretable model with a high probability of becoming a clinically practical tool in identifying brain tumors in the real world.