ENHANCED BRAIN CANCER DETECTION IN MRI SCANS THROUGH TEMPLATE REGRESSION SIAMESE REGIONAL PROPOSED NETWORK FOR SEGMENTATION AND FUZZY LOGIC FUSION OF SEGMENTED REGIONS

ICTACT Journal on Image and Video Processing ( Volume: 15 , Issue: 1 )

Abstract

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 sub- tracklets 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.

Authors

S.A. Anlet Sharmili1, M. Sankari2
Manonmaniam Sundaranar University, India1, Lekshmipuram College of Arts and Science, India2

Keywords

Brain Cancer Detection, MRI Scans, Siamese Regional Proposed Network, Bounding Box Regression, Fuzzy Logic Fusion

Published By
ICTACT
Published In
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 1 )
Date of Publication
August 2024
Pages
3347 - 3356
Page Views
298
Full Text Views
19

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