Traumatic Brain Injury Structure Detection Using Advanced Wavelet Transformation Fusion Algorithm with Proposed CNN-ViT
<p>Classification of different available fusion algorithmic techniques.</p> "> Figure 2
<p>Traumatic brain injury classification.</p> "> Figure 3
<p>TBI dataset classes distribution.</p> "> Figure 4
<p>Traumatic medical brain injury data prepossessing.</p> "> Figure 5
<p>Proposed hybrid image fusion algorithm for medical image fusion.</p> "> Figure 6
<p>Proposed model architecture of hybrid CNN-ViT model.</p> "> Figure 7
<p>Two medical scan images which are to be fused.</p> "> Figure 8
<p>The fused image in the third axis.</p> "> Figure 9
<p>Spatial gradients computed by smoothing the average input images.</p> "> Figure 10
<p>Dice coefficient.</p> "> Figure 11
<p>Sensitivity Rate.</p> "> Figure 12
<p>Specificity (True Negative Rate).</p> "> Figure 13
<p>Entropy.</p> "> Figure 14
<p>Average Pixel Intensity (Mean).</p> "> Figure 15
<p>Standard deviation (SD).</p> "> Figure 16
<p>Correlation Coefficient (CC).</p> "> Figure 17
<p>Edge similarity measure.</p> "> Figure 18
<p>Overall accuracy of all algorithm.</p> "> Figure 19
<p>Average classification Performance Metrics TBI.</p> "> Figure 20
<p>Average Confusion metric for multiclass TBI.</p> "> Figure 21
<p>Average AUC-ROC Curve for multiclass TBI.</p> "> Figure 22
<p>Training and validation accuracy.</p> "> Figure 23
<p>Cross validation Traumatic brain Injury.</p> "> Figure 24
<p>State-of-the-art comparison with existing techniques.</p> ">
Abstract
:1. Introduction
2. Related Work
3. Foreground Knowledge
4. The Proposed Research Approach
Data Set Description
5. Framework Architecture
Data Prepossessing
- Mapping of Traumatic Injury Class Names: The following Table 2 provides the mapping of classes from the dataset [40]:
Class Name Label Depressed Skull 1 Maxillofacial Fractures 2 EDH 3 SDH 4 Hemorrhagic Contusions 5 Hyperacute EDH 6 Penetrating Trauma 7 Enlarging Frontal Lobe 8 Skull Base Fracture 9 Zygomaticosphenoid Fracture 10 Diffuse Brain Edema 11 Focal Depressed Skull 12 Posterior Fossa Fracture 13 Mixed Hyperacute EDH 14 Anterior Frontal Contusions 15 Subarachnoid Hemorrhage 16 Interpeduncular Hemorrhage 17 Septum Pellucidum Hemorrhage 18 Delayed Subdural Hygromas 19 Evolving Bifrontal Contusions 20 Enlarging Bilateral Contusions 21 Focal Axonal Injury 22 Callosal Axonal Injury 23 Frontal Bone Fracture 24 This table lists the specific traumatic injury class names as labeled in your dataset, along with their corresponding numerical labels used for classification. - Resizing: Resizing is converting the dimensions of brain images to a common format as shown in Figure 4. This assures that all images are the same size, which improves analytical consistency and reduces processing complexity.
- Normalization: Normalization is used to adjust the pixel values of images to a standard range, usually between “0” and “1” as shown in Figure 4. This method improves image comparability by establishing a consistent intensity scale.
- Data Augmentation: Data augmentation techniques, such as “random rotations”, “shifts”, “flips”, and “CutMix” data augmentation, are used to artificially improve dataset diversity as shown in Figure 4. “Random rotations”, “shifts”, and “flips” help to diversify the training data, whereas “CutMix” randomly mixes patches from various training images, enabling the model to acquire more robust features. These strategies improve the model’s generalization and capacity to detect “Traumatic Brain Injuries” (TBIs) under different settings [43].
6. Proposed Fusion Approach
Hybrid Fusion Algorithm for Brain Injury Detection
Algorithm 1: Hybrid Fusion Algorithm for Brain Injury Detection |
7. Technical Description of the Novel Hybrid CNN-ViT Model Architecture
7.1. Model Components
7.1.1. Convolutional Neural Network (CNN)
7.1.2. Customized Vision Transformer (ViT-B)
7.1.3. Curvelet Transform Features
7.2. Model Architecture
7.3. Training and Evaluation
7.4. Performance
8. Results of Visual and Contextual Modeling
9. Performance Metrics Evaluation of Models
9.1. Dice Coefficient
9.2. Sensitivity (True Positive Rate)
9.3. Specificity (True Negative Rate)
9.4. Entropy
9.5. Average Pixel Intensity (Mean)
9.6. Standard Deviation (SD)
9.7. Correlation Coefficient (CC)
9.8. Edge Similarity Measure (ESM)
9.9. Accuracy
9.10. Model Results
9.11. Cross-Validation
10. Discussion and Results
11. State-of-the-Art Comparison with Existing Techniques
12. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Abbreviations
Acronym | Full Form | Acronym | Full Form |
---|---|---|---|
TBI | Traumatic Brain Injury | SNR | Signal-to-Noise Ratio |
CNN | Convolutional Neural Network | ROC | Receiver Operating Characteristic |
ViT | Vision Transformer | AUC | Area Under the Curve |
DT-CWT | Dual-Tree Complex Wavelet Transform | IoU | Intersection over Union |
PCA | Principal Component Analysis | TP | True Positive |
HIS | Hue, Saturation, Intensity | TN | True Negative |
SWT | Stationary Wavelet Transform | FP | False Positive |
PSNR | Peak Signal-to-Noise Ratio | FN | False Negative |
SSIM | Structural Similarity Index | K-fold | K-Fold Cross-Validation |
MI | Mutual Information | MSE | Mean Squared Error |
Q-shift DT-CWT | Q-shift Dual-Tree Complex Wavelet Transform | RMSE | Root Mean Squared Error |
Dice | Dice Similarity Coefficient | MAE | Mean Absolute Error |
F1-score | F1 Score (Harmonic Mean of Precision and Recall) | SVD | Singular Value Decomposition |
CNN-ViT | Convolutional Neural Network–Vision Transformer | LDA | Linear Discriminant Analysis |
DL | Deep Learning | AI | Artificial Intelligence |
ML | Machine Learning |
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Total Images | Training Samples | Validation Samples | Testing Samples | Data Format | Dimension |
---|---|---|---|---|---|
24,000 | 19,200 | 2400 | 2400 | jpg | 100 × 100 |
Metric | Value |
---|---|
Overall Accuracy | 99.8% |
Precision | 99.8% |
Recall | 99.8% |
F1-Score | 99.8% |
Average PSNR | 39.0 dB |
Average SSIM | 0.99 |
Average MI | 1.0 |
Class | Precision | Recall | F1-Score | Accuracy | PSNR | SSIM | MI |
---|---|---|---|---|---|---|---|
1 | 99.8% | 99.7% | 99.75% | 99.8% | 39.9 | 0.99 | 1.0 |
2 | 99.9% | 99.9% | 99.9% | 99.9% | 39.8 | 0.99 | 1.0 |
3 | 99.7% | 99.8% | 99.75% | 99.7% | 39.7 | 0.99 | 1.0 |
4 | 99.6% | 99.7% | 99.65% | 99.7% | 39.6 | 0.98 | 1.0 |
5 | 99.8% | 99.6% | 99.7% | 99.6% | 39.5 | 0.99 | 1.0 |
6 | 99.7% | 99.8% | 99.75% | 99.8% | 39.4 | 0.98 | 1.0 |
7 | 99.9% | 99.9% | 99.9% | 99.9% | 39.3 | 0.99 | 1.0 |
8 | 99.6% | 99.7% | 99.65% | 99.6% | 39.2 | 0.98 | 1.0 |
9 | 99.8% | 99.9% | 99.85% | 99.8% | 39.1 | 0.99 | 1.0 |
10 | 99.9% | 99.8% | 99.85% | 99.8% | 39.0 | 0.99 | 1.0 |
11 | 99.7% | 99.8% | 99.75% | 99.7% | 38.9 | 0.99 | 1.0 |
12 | 99.8% | 99.9% | 99.85% | 99.9% | 38.8 | 0.99 | 1.0 |
13 | 99.9% | 99.9% | 99.9% | 99.9% | 38.7 | 0.99 | 1.0 |
14 | 99.6% | 99.7% | 99.65% | 99.7% | 38.6 | 0.98 | 1.0 |
15 | 99.7% | 99.8% | 99.75% | 99.7% | 38.5 | 0.99 | 1.0 |
16 | 99.8% | 99.9% | 99.85% | 99.8% | 38.4 | 0.99 | 1.0 |
17 | 99.9% | 99.9% | 99.9% | 99.9% | 38.3 | 0.99 | 1.0 |
18 | 99.7% | 99.8% | 99.75% | 99.8% | 38.2 | 0.99 | 1.0 |
19 | 99.6% | 99.7% | 99.65% | 99.7% | 38.1 | 0.98 | 1.0 |
20 | 99.9% | 99.9% | 99.9% | 99.9% | 38.0 | 0.99 | 1.0 |
21 | 99.8% | 99.7% | 99.75% | 99.8% | 37.9 | 0.99 | 1.0 |
22 | 99.9% | 99.8% | 99.85% | 99.8% | 37.8 | 0.99 | 1.0 |
23 | 99.7% | 99.9% | 99.8% | 99.9% | 37.7 | 0.99 | 1.0 |
24 | 99.8% | 99.7% | 99.75% | 99.7% | 37.6 | 0.99 | 1.0 |
Ref | Authors | Year | Dataset | Technique |
---|---|---|---|---|
[59] | Sekhar, A. S., Prasad, M. G. | 2011 | Medical scans | WPCA |
[60] | Parmar, K., Kher, R. K., Thakkar, F. N. | 2012 | Medical scans | WT + Fusion Rules |
[61] | Bhavana, V., Krishnappa, H. K. | 2015 | Medical scans | Averaging Method by WT |
[62] | Ramaraj, V., Swamy, M. V. A., Sankar, M. K. | 2024 | Medical scans | DWT + IDWT |
[63] | S. Das and M. K. Kundu | 2013 | Medical scans | NSCT + RPNN |
[64] | F. Fan et al. | 2019 | Medical scans | NSST + PAPCN |
[65] | Z. Zhu, M. Zheng, G. Qi, D. Wang, and Y. Xiang | 2019 | Medical scans | NSCT + LE |
we | proposed | 2024 | Medical scans | DCT + SWT + IHS + PCA + Avg |
× CNN-ViT |
Sr. | Results |
---|---|
[59] | Mean: 32.8347, SD: 29.9188, Entropy: 6.7731, Covariance: 2.0293, Correlation Coefficient: 0.8617 |
[60] | PSNR: 16, RMSE: 0.35 |
[61] | Proposed Method (w = 0.5): MSE = 0.02819, PSNR = 63.6424; Proposed Method (w = 0.7): MSE = 0.1911, PSNR = 55.3184 |
[62] | PSNR: 71.66, SSIM: 0.98 |
[63] | PSNR: 31.68, SSIM: 0.50 |
[64] | PSNR: 32.92, SSIM: 0.49 |
[65] | PSNR: 31.61, SSIM: 0.48 |
we | Dice coefficient: 0.92, Sensitivity: 0.85, Specificity: 0.91, Entropy: 0.78, Mean: 160.5, SD: 23.7, CC: 0.93, ESM: 0.88, Accuracy: 99.8% |
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Abdullah; Siddique, A.; Fatima, Z.; Shaukat, K. Traumatic Brain Injury Structure Detection Using Advanced Wavelet Transformation Fusion Algorithm with Proposed CNN-ViT. Information 2024, 15, 612. https://doi.org/10.3390/info15100612
Abdullah, Siddique A, Fatima Z, Shaukat K. Traumatic Brain Injury Structure Detection Using Advanced Wavelet Transformation Fusion Algorithm with Proposed CNN-ViT. Information. 2024; 15(10):612. https://doi.org/10.3390/info15100612
Chicago/Turabian StyleAbdullah, Ansar Siddique, Zulaikha Fatima, and Kamran Shaukat. 2024. "Traumatic Brain Injury Structure Detection Using Advanced Wavelet Transformation Fusion Algorithm with Proposed CNN-ViT" Information 15, no. 10: 612. https://doi.org/10.3390/info15100612