ISSN No:2250-3676 ----- Crossref DOI Prefix: 10.64771
Scholarly Peer Reviewed and Fully Referred Open Access Multidisciplinary Monthly Research Journal
A HYBRID DBIM–CNN FRAMEWORK FOR PRECISE BRAIN STROKE SEGMENTATION IN MICROWAVE IMAGINGID: 1836 Abstract :Microwave Medical Imaging (MMI) Offers Deeper Tissue Visibility, But Traditional DBIM-based Segmentation Struggles With Noise And Unclear Boundaries. To Enhance Accuracy, This Work Integrates Convolutional Neural Networks (CNNs) With DBIM Reconstruction To Automatically Learn Complex Stroke Features And Refine Segmentation. The Hybrid DBIM–CNN Model Improves Boundary Detection, Increases Robustness Across Datasets, And Reduces Manual Intervention. This Approach Enables Faster, More Precise Brain Stroke Diagnosis For Reliable Clinical Decision-making. Index Terms - — Microwave Medical Imaging (MMI), Brain Stroke Diagnosis, DBIM Reconstruction, Image Segmentation, Convolutional Neural Networks (CNNs), Deep Learning, Stroke Boundary Detection, Medical Image Processing, Noise-Resistant Segmentation, Hybrid Imaging Model |
Published:04-7-2025 Issue:Vol. 25 No. 7 (2025) Page Nos:971-978 Section:Articles License:This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. How to Cite |