Enhanced Brain Cancer Detection in MRI Scans Through Template Regression Siamese Regional Proposed Network for Segmentation and Fuzzy Logic Fusion of Segmented Regions

Accurate detection and segmentation of brain cancer in MRI scans are critical for effective diagnosis and treatment planning. Traditional methods often struggle with the complexities of tumor morphology and variations in scan quality. Existing detection systems can be slow and may not effectively handle the variability in tumor appearances, leading to potential delays in diagnosis and treatment. To address these challenges, we propose an enhanced detection framework using a Siamese Regional Proposed Network (SRPN). The SRPN integrates template branch and bounding box regression to expedite detection processes. The system utilizes an extended Siamese network to learn the distance between tracklet pairs, capturing the local and global features of tumors. These features are transferred to bidirectional gated recurrent units (GRUs), which generate tracklets and segment them into shorter sub-tracklets based on local distances. The segmented subtracklets are then reconnected into longer trajectories using similarities derived from temporal pooling global features. Additionally, fuzzy logic fusion is employed to combine segmented regions for improved accuracy. The SRPN-based framework demonstrated a significant improvement in detection speed and accuracy. Experimental results show an accuracy increase of 12% over traditional methods, achieving 94% accuracy with a detection time reduction of 30%. The system also improved segmentation precision, with a mean Intersection over Union (IoU) score of 85%, compared to 75% in conventional approaches.