MFWX: Multi-Scale CNN with Multi-Frequency Channel Attention and Weighted Particle Swarm Optimization for Enhanced Brain Tumor Segmentation and Classification

 

 

 

Mohammed Nazneen Fathima1,*, Prabhjot Singh1, Simrandeep Singh1

 

1Department of Electronic and Communication Engineering, Chandigarh University, Mohali, Punjab, India

 

Emails: nazneenfathima2@gmail.com; parulpreet.23367@lpu.co.in; staff.simrandeep.singh@iitrpr.ac.in

 

Text Box: Abstract The research-automated segmentation of brain tumors occurs due to the need to enhance diagnosis and/or treatment planning. The existing techniques suffer the effects of scale variation, redundant features, and the high dimensionality that causes ambiguous findings. We suggest the model named MFWX, which unites Multi-Scale CNN, Multi-Frequency Channel Attention (MFCA), Weighted Particle Swarm Optimization (WPSO) to identify features and XGBoost methods to classify them. The Multi-Scale CNN will capture the structure of the tumor at multiple resolutions, MFCA adjusts the features by zeroing in on significant frequency zones and WPSO eliminates redundancy to heavy-hit the strong forecasts of XGBoost. However, MFWX attained 94.2 accuracy and 92.5 Dice on the BraTS-2020 dataset surpassing ResNet50, EfficientNet-B7, and U-Net. It achieved an accuracy of 96.7%, and Dice of 95.1% on BraTS-2018, and performed well on classes of tumors. Ablation experiments proved the necessity of every part. In general, MFWX presents an efficient, clinically meaningful, scalable solution that outsmarts the current segmentation techniques.

 

Received: January 27, 2025 Revised: March 18, 2025 Accepted: June 19, 2025

 

Keywords: Brain; Transformation; Deep learning; Segmentation; Classification