Traumatic Brain Injury Structure Detection Using Advanced Wavelet Transformation Fusion Algorithm with Proposed CNN-ViT

• Published in: Information (Basel) Vol. 15; no. 10; p. 612
• Main Authors: Abdullah, Siddique, Ansar, Fatima, Zulaikha, Shaukat, Kamran
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Accuracy; Algorithms; Artificial neural networks; Brain; Brain research; Classification; Comparative analysis; Correlation coefficients; Data mining; Datasets; Decision making; Decision trees; Deep learning; Disease; Head injuries; Hemorrhage; Image enhancement; image fusion; Image quality; Injuries; Injury prevention; Machine learning; max–min; Medical diagnosis; Medical imaging; Medical imaging equipment; Methods; Mortality; simple average; spatial and frequency domain; Traumatic brain injury; wavelet; Wavelet transforms; weighted
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info15100612

Detecting Traumatic Brain Injuries (TBI) through imaging remains challenging due to limited sensitivity in current methods. This study addresses the gap by proposing a novel approach integrating deep-learning algorithms and advanced image-fusion techniques to enhance detection accuracy. The method combines contextual and visual models to effectively assess injury status. Using a dataset of repeat mild TBI (mTBI) cases, we compared various image-fusion algorithms: PCA (89.5%), SWT (89.69%), DCT (89.08%), HIS (83.3%), and averaging (80.99%). Our proposed hybrid model achieved a significantly higher accuracy of 98.78%, demonstrating superior performance. Metrics including Dice coefficient (98%), sensitivity (97%), and specificity (98%) verified that the strategy is efficient in improving image quality and feature extraction. Additional validations with “entropy”, “average pixel intensity”, “standard deviation”, “correlation coefficient”, and “edge similarity measure” confirmed the robustness of the fused images. The hybrid CNN-ViT model, integrating curvelet transform features, was trained and validated on a comprehensive dataset of 24 types of brain injuries. The overall accuracy was 99.8%, with precision, recall, and F1-score of 99.8%. The “average PSNR” was 39.0 dB, “SSIM” was 0.99, and MI was 1.0. Cross-validation across five folds proved the model’s “dependability” and “generalizability”. In conclusion, this study introduces a promising method for TBI detection, leveraging advanced image-fusion and deep-learning techniques, significantly enhancing medical imaging and diagnostic capabilities for brain injuries.