Enhanced magnetic resonance imaging feature extraction for precise brain tumor classification using dual deep convolutional networks

Abstract

Precise and reliable classification of brain tumors is a critical prerequisite for effective medical diagnostics and the development of targeted treatment strategies. The complex and diverse structures of brain tumors such as their texture, size, and appearance pose significant challenges for deep learning models, often reducing their accuracy in identifying tumors from magnetic resonance imaging scans. To tackle this challenge, we introduce the Dual Deep Convolutional Brain Tumor Network, which combines a pre-trained Visual Geometry Group 19 model with a custom-designed Convolutional Neural Network to extract both fine-grained and high-level tumor features. By combining these complementary feature sets, the model enhances classification accuracy and robustness, providing a comprehensive understanding of the complex brain tumor landscape. The model’s effectiveness was validated through 10-fold cross-validation using the Kaggle brain tumor classification dataset, encompassing glioma, no tumor, meningioma, and pituitary categories. Experimental findings reveal that our model surpasses existing techniques, attaining 98.81 % accuracy, 97.69 % precision, 97.75 % recall, 99.18 % specificity, and an F1-score of 97.70 %. These results confirm that the integrated model provides a reliable and accurate solution for brain tumor classification, with significant implications for clinical diagnostics and treatment planning.