Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Data Preprocessing
- Image Resizing: Each image was resized to a standard dimension, certifying uniformity across the dataset. This was important for consistency when feeding the images into the neural network models.
- Grayscale Conversion: The resized images were converted to grayscale to moderate computational complexity. Color information was typically redundant for the task at hand, as the focus was on texture and shape within the images.
- Gaussian Blurring: To reduce high-frequency noise, a Gaussian blur was applied to the grayscale images. This smoothing technique aided in highlighting the more significant structures within the brain by softening edges and details.
- Otsu’s Thresholding: We implemented Otsu’s thresholding to separate the brain tissue from the background. This method automatically computed a threshold value for image binarization, which was employed to detect contours.
- Contour Detection and Selection: By applying contour detection, we identified the boundaries of all the objects in the binary image. We assumed the largest contour to be the brain’s boundary, a reasonable assumption in a typical MRI scan.
- Extreme Points and Cropping: Once the largest contour was identified, we verified its extreme points. These points represented the furthest pixels in the horizontal and vertical directions within the contour, which we employed to define a cropping boundary.
- Image Cropping: The image was cropped using the extreme points as vertices, with an optional padding added to ensure no part of the brain was excluded.
- Displaying the Process: We visualized the preprocessing steps, displaying the original image, the contour of the largest detected region, the extreme points, and finally, the cropped brain region.
3.2. Optimization Method
- Optimization of Data Augmentation Parameters: MVO was utilized to uncover the best data augmentation techniques and their respective parameter values. This step was crucial to enhance the dataset variability without deviating from the realistic transformations applicable to MRI images. By doing so, we aimed to maximize the performance of the classification models while avoiding overfitting.
- Layer-wise Trainability in CNNs: We also applied the MVO strategy to find the optimal configuration of trainable layers within VGG16 and ResNet50 architectures. The method helped in identifying which layers should be frozen (weights not updated during training) and which should be trainable (weights updated during training) to improve the models’ accuracy and efficiency. This is particularly important, as deep neural networks can be computationally expensive to train, and freezing certain layers can significantly diminish the number of parameters that are essential to be updated, thus speeding up the training process and potentially improving generalization.
- Universe Size (Population Size): This parameter defines the number of potential solutions (universes) that the algorithm will consider simultaneously. A larger universe size allows for greater exploration of the solution space. A universe size of 30 achieves a compromise between computing efficiency and diversity within the search space. More extensive populations enhance diversity but may impede the optimization process, whereas smaller populations might converge rapidly yet risk overlooking ideal solutions due to inadequate research. A universe size of 30 facilitates a sufficiently diversified population to investigate many solutions while minimizing computational expenses.
- Wormhole Existence Probability (WEP): This probability controls the creation of wormholes, which are mechanisms for sharing information between universes. A value of 0.6 signifies a considerable probability of the formation of these wormholes. This likelihood facilitates an effective combination of exploration (investigating new regions of the solution space) and exploitation (enhancing current solutions). A high WEP (approaching 1) may result in premature convergence due to excessive emphasis on exploitation, whereas a low WEP (approaching 0) restricts exploitation, hence obstructing convergence to an ideal solution. A WEP of 0.6 guarantees that the MVO algorithm can efficiently navigate the solution space while simultaneously enhancing viable solutions.
- Travelling Distance Rate (TDR): This rate determines how much a solution can be altered when a wormhole event occurs. A value of 0.4 signifies that alterations to the solutions are moderate. If the TDR is overly elevated, solutions may be excessively disturbed, resulting in irregular exploration that could overlook excellent solutions. If TDR is excessively low, the algorithm may adopt an overly conservative approach and become trapped in local optima. A TDR of 0.4 offers a balanced methodology, enabling the algorithm to implement significant modifications to solutions while remaining close to attractive regions.
- Maximum/Minimum WEP: These parameters set the lower and upper bounds for the wormhole existence probability, introducing dynamic variability in the algorithm. The interval [0.2, 1.0] guarantees that during the initial phases of optimization, the method prioritizes exploration (with a reduced WEP at 0.2), thus avoiding premature convergence. As the algorithm advances, WEP approaches 1.0, highlighting exploitation in the latter phases to optimize the results. The incremental rise in WEP enables the early investigation of several options while refining the search in the concluding iterations for enhanced precision.
- Maximum/Minimum TDR: Similar to WEP, these parameters set the boundaries for the traveling distance rate, controlling the extent of solution alterations through the optimization process. Commencing with a minimum TDR of 0.4 guarantees that initial exploration remains closely aligned with the existing optimal solutions. As optimization progresses, the TDR may rise to 1.0, facilitating more substantial alterations in subsequent phases. This adaptive modification allows the method to maintain flexibility throughout the initial phases of optimization while intensifying solution refinement as it nears convergence.
3.3. Data Augmentation
- Mitigates Overfitting: Augmentation spreads the variety of the training samples, which helps prevent the model from memorizing specific images and overfitting.
- Improves Generalization: By simulating various scenarios, data augmentation allows the model to generalize better to new, unseen data.
- Compensates for Imbalanced Datasets: In cases where some classes are underrepresented, augmentation can help to balance the samples without the need to collect more data.
- Enhances Model Robustness: Augmented data aid the model in learning more robust features that are invariant to certain transformations, which is significant for real-world applications.
3.4. Model Building with VGG16 and ResNet50
3.5. Model Training
3.6. Dataset
- 1.
- No Tumor:
- This class encompasses brain MRI scans attained from individuals who do not demonstrate any detectable brain tumors. These scans serve as a crucial baseline for comparative analysis against scans that do depict tumors.
- Individuals within this class are typically those seeking routine brain examinations or experiencing neurological symptoms unrelated to tumor presence.
- 2.
- Tumor:
- The “Tumor” class comprises brain MRI scans from individuals who have been clinically diagnosed with brain tumors. Brain tumors are marked in various locations, sizes, and types, making their accurate finding and grouping a formidable challenge in the field of medical imaging.
- The tumors within this class may include metastatic tumors, gliomas, meningiomas, and other forms of abnormal growth within the brain.
3.7. Model Evaluation
- 1.
- Precision (Equation (1)):
- Description: Precision assesses the accuracy of the positive predictions made by the structure. In simpler terms, it answers the following question: of all the instances where the model predicted ‘positive’, how many were actually positive?
- Components:
- True Positives (TP): These are the instances where the model precisely predicts the positive class.
- False Positives (FP): These are the instances where the model wrongly predicts the positive class.
- Usage: Precision is particularly essential in situations where the cost of a false positive is high. For example, in medical diagnostics, falsely recognizing a healthy patient as having a tumor can lead to unnecessary stress and treatment.
- 2.
- Accuracy (Equation (2)):
- Description: Accuracy measures the proportion of true outcomes (both true negatives and positives) among the total number of cases examined. It essentially quantifies how often the model is correct.
- Components:
- True Negatives (TN): These are the instances where the model properly predicts the negative class.
- False Negatives (FN): These are the instances where the model mistakenly predicts the negative class.
- Usage: Accuracy is a valuable measure when the classes in the dataset are balanced. However, its usefulness is reduced when dealing with imbalanced datasets, as it can be misleadingly high in cases where the model mostly predicts the majority class correctly.
- 3.
- Recall (Sensitivity) (Equation (3)):
- Description: Recall measures the proportion of actual positives that were accurately recognized by the model. It answers the following question: of all the instances that were actually positive, how many did the model identify?
- Usage: Recall is critical in contexts where missing a positive instance is significantly worse than falsely detecting a negative instance as positive. For example, in medical diagnostics, a false negative (failing to detect a tumor) can be more dangerous than a false positive.
4. Results and Discussion
5. Conclusions and Outlook
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Hyperparameters | Description | Best Value |
---|---|---|
Universe Size | Number of solutions in the population | 30 |
Wormhole Existence Probability (WEP) | Probability of wormholes’ appearance | 0.6 |
Travelling Distance Rate (TDR) | How far a wormhole can alter a solution | 0.4 |
Max/Min WEP | Bounds the occurrence of wormholes | Max: 1.0, Min: 0.2 |
Max/Min TDR | Limits the modification extent by the wormholes | Max: 1.0, Min: 0.4 |
Index | Augmentation Method | Description | Used in Study | Suggested Values by MVO Optimizer |
---|---|---|---|---|
1 | Rotation range | Random rotation within a specified range of degrees. | Yes | 10 |
2 | Width Shifting range | Random horizontal shift. | Yes | 0.1 |
3 | Height Shifting range | Random vertical shift. | Yes | 0.1 |
4 | Shear range | Random shearing. | Yes | 0.1 |
5 | Brightness range | Random brightness adjustments. | Yes | [0.5…1.5] |
6 | Horizontal Flip | Random horizontal flipping. | Yes | True |
7 | Vertical Flip | Random vertical flipping. | Yes | True |
8 | Zoom range | Random zooming into the images. | Yes | 0.1 |
9 | Channel Shift * | Random channel shifting. | No | - |
10 | Fill Mode * | Method for filling points outside the boundaries of the input. | No | - |
Data | Model | Accuracy (%) | Precision (%) | Recall (%) | Run Results |
---|---|---|---|---|---|
Validation | VGG16 | 89 | 91 | 96 | |
Test | 88 | 92 | 94 | ||
Validation | ResNet50 | 92 | 95 | 96 | |
Test | 92 | 96 | 94 | ||
Validation | VGG16 + ResNet50 | 92 | 93 | 98 | |
Test | 89 | 92 | 96 | ||
Validation | VGG16 + ResNet50 + MVO | 93 | 97 | 95 | |
Test | 94 | 97 | 96 |
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Sarshar, N.T.; Sadeghi, S.; Kamsari, M.; Avazpour, M.; Ghoushchi, S.J.; Ranjbarzadeh, R. Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method. BioMed 2024, 4, 499-523. https://doi.org/10.3390/biomed4040038
Sarshar NT, Sadeghi S, Kamsari M, Avazpour M, Ghoushchi SJ, Ranjbarzadeh R. Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method. BioMed. 2024; 4(4):499-523. https://doi.org/10.3390/biomed4040038
Chicago/Turabian StyleSarshar, Nazanin Tataei, Soroush Sadeghi, Mohammadreza Kamsari, Mahrokh Avazpour, Saeid Jafarzadeh Ghoushchi, and Ramin Ranjbarzadeh. 2024. "Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method" BioMed 4, no. 4: 499-523. https://doi.org/10.3390/biomed4040038
APA StyleSarshar, N. T., Sadeghi, S., Kamsari, M., Avazpour, M., Ghoushchi, S. J., & Ranjbarzadeh, R. (2024). Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method. BioMed, 4(4), 499-523. https://doi.org/10.3390/biomed4040038