Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/4141 2019 2019 Department of Electronic and Communication Engineering, Chandigarh University, Mohali, Punjab, India Mohammed Mohammed Department of Electronic and Communication Engineering, Chandigarh University, Mohali, Punjab, India Prabhjot Singh1 Department of Electronic and Communication Engineering, Chandigarh University, Mohali, Punjab, India Simrandeep Singh The research-automated segmentation of brain tumors occurs due to the need to enhance diagnosis andor 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. 2025 2025 325 339 10.54216/JISIoT.170221 https://www.americaspg.com/articleinfo/18/show/4141