MPFAN: A Novel Multiscale Network for Brain Tumor MRI Classification

Introduction Accurate classification of brain tumours using MRI scans is vital for early diagnosis and treatment. However, conventional deep learning models often require complete MRI sequences, which can prolong scan times and lead to patient discomfort or motion-related image degradation. Thus, enhancing diagnostic accuracy under faster scanning conditions is a critical research need. Therefore, this research aims to show how our proposed mechanism, namely Multiscale Parallel Feature Aggregation Network (MPFAN), accurately improves the diagnosis of classifying brain tumours while maintaining Magnetic Resonance Imaging (MRI) quality in fast MRI scanning. Methods This article proposed an MPFAN architecture that utilizes parallel branches to extract image features from different scales, using independent pathways with varied filters and movement steps. Feature combination blocks, feedback prevention mechanisms, and strict training constraints enhance system reliability. Results MPFAN achieved an accuracy of 97.4%, outperforming many existing brain tumour classification models. Performance improved steadily over training epochs, and optimizer comparisons showed Adam and Ada-Delta yielded the best results. Ablation studies confirmed that multiscale feature extraction, dropout regularization, and feature fusion significantly contribute to classification accuracy. Discussion The MPFAN model demonstrates superior performance due to its ability to effectively extract and integrate multiscale features. Its dual-branch architecture enables deeper contextual understanding, and its high accuracy validates its clinical potential. However, the model’s reliance on a single dataset and potential overfitting in later training epochs indicate the need for broader validation and optimization in real-world clinical environments. Conclusion The proposed MPFAN architecture enhances brain tumour classification by improving image processing efficiency and decision-making speed, making it a reliable and effective diagnostic tool.