Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning
Abstract
:1. Introduction
1.1. Image Denoising Perspective
1.2. Transfer Learning Perspective
1.3. Identified Research Gaps
How Our Work Addresses These Gaps
1.4. Objective of the Proposed Work
- Utilize Fractional Order Techniques: Implement fractional order techniques in the ETLFOD model to overcome the limitations of traditional denoising methods, enhancing the robustness and accuracy of noise removal in medical images.
- Leverage Transfer Learning: Use transfer learning to incorporate pre-trained models such as CNN, VGG16, DenseNet121, and Inception V3, which are trained on large-scale datasets, to significantly improve the denoising performance while preserving important image features.
- Mitigate Overfitting: Improve both training and validation accuracy to mitigate overfitting, ensuring the ETLFOD model generalizes well to new, unseen medical image data.
- Validate with Diverse Datasets: Utilize a variety of medical imaging datasets, including MRI, CT scans, and X-ray images, to validate the ETLFOD model’s ability to accurately identify infected areas and confirm its robustness across different medical conditions.
- Comprehensive Performance Comparison: Compare the ETLFOD model’s performance with existing models using multiple evaluation metrics such as sensitivity, specificity, and accuracy, aiming for an improvement of at least 6% over current state-of-the-art techniques. To have a more comprehensive analysis, we evaluated the proposed model on all measurement data and performed 10-fold cross-validation to reduce the bias distribution of the image model. We further provide a detailed discussion of the results to enhance our understanding of the model’s performance.
2. Proposed System
2.1. Benchmark Dataset Description
2.2. System Description
2.3. An Efficient Fractional-Order-Based Image Denoising
- Riemann–Liouville Derivative:
- Caputo Derivative:
- Grünwald–Letnikov Derivative:
Optimization Problem for Image Processing
2.4. Distribution of Pixel Intensity to Calculate Mean and Standard Deviation
2.4.1. Discussion on MRI Brain Images
2.4.2. Discussion on Lung CT Images
2.4.3. Discussion on Pneumonia X-ray Images
2.5. Transfer Learning
2.5.1. Convolutional Neural Networks
Detailed Description of the Custom CNN Architecture in ETLFOD Model
- Custom CNN Architecture OverviewInput Layer:
- ○
- Description: The input layer accepts medical images with dimensions H × W × CH\times W\times CH × W × C, where HHH is the height, WWW is the width, and CCC is the number of channels (e.g., grayscale or RGB).
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- Input Shape: Typically, 224 × 224 × 3224\times 224\times 3224 × 224 × 3 for RGB images.
- Convolutional Layers: First Convolutional Layer:
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- Filter Size: 32 filters
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- Kernel Size: 3 × 33\times 33 × 3
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- Activation Function: ReLU (Rectified Linear Unit)
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- Stride: 1
- ○
- Padding: Same
- Second Convolutional Layer:
- ○
- Filter Size: 64 filters
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- Kernel Size: 3 × 33\times 33 × 3
- ○
- Activation Function: ReLU
- ○
- Stride: 1
- ○
- Padding: Same
- Third Convolutional Layer:
- ○
- Filter Size: 128 filters
- ○
- Kernel Size: 3 × 33\times 33 × 3
- ○
- Activation Function: ReLU
- ○
- Stride: 1
- ○
- Padding: Same
- Pooling Layers: First Pooling Layer:
- Type: Max Pooling
- Pool Size: 2 × 22\times 22 × 2
- Stride: 2
- Second Pooling Layer:
- Type: Max Pooling
- Pool Size: 2 × 22\times 22 × 2
- Stride: 2
- Batch Normalization:
- Batch normalization layers are added after each convolutional layer to normalize the inputs and improve training stability and performance.
- Dropout Layers: First Dropout Layer:
- Dropout Rate: 0.25 (applied after the first pooling layer)
- Second Dropout Layer:
- Dropout Rate: 0.5 (applied after the second pooling layer)
- Fully Connected (Dense) Layers: First Dense Layer:
- Units: 512
- Activation Function: ReLU
- Second Dense Layer:
- Units: 256
- Activation Function: ReLU
- Output Layer:
- Units: Number of classes (for classification tasks) or 1 (for regression tasks)
- Activation Function: Softmax (for classification) or Linear (for regression)
- Custom CNN ArchitectureThe custom CNN architecture for the ETLFOD model is based on the principles of transfer learning, employing several well-known pre-trained models. The detailed architecture for each of these models is as follows:DenseNet121:Architecture: Each layer in DenseNet121 is connected to every other layer in a feed-forward fashion, allowing for maximum information flow between layers. This connectivity pattern is designed to improve gradient flow and make the network more efficient.Parameters: Uses multiple dense blocks, each containing several convolutional layers.Pretraining Dataset: ImageNetEquation: y = F(x,W)Fine-Tuning: The pre-trained model is fine-tuned on the target dataset.VGG16:Architecture: Composed of 2–3 convolutional layers followed by a pooling layer. Includes two hidden layers with 4096 nodes each and an output layer with 1000 nodes. Uses 3 × 3 filters for convolutional layers.Parameters: A total of 13 convolutional layers, 5 pooling layers, 3 fully connected layers.Pretraining Dataset: ImageNet.ResNet50:Architecture: Fifty layers deep with residual blocks. The architecture includes convolutional layers, batch normalization, ReLU activation, and max pooling.Parameters: A total of 23 convolutional layers and 1 fully connected layer. The filters increase from 64 to 256 in each group of residual blocks.Pretraining Dataset: ImageNetEquation: The network uses residual learning with shortcut connections to jump over some layers.Inception V3:Architecture: Designed with inception modules that perform convolutions in parallel, including 1 × 1, 3 × 3, and 5 × 5 convolutions followed by max pooling.Parameters: Multiple inception modules with batch normalization and auxiliary classifiers.Pretraining Dataset: ImageNet.Pretraining Dataset
- For transfer learning, our custom CNN architecture was initialized using pre-trained weights from a widely recognized dataset. The specific pretraining dataset and the process are as follows:
- Algorithm Steps:
- Apply Gaussian noise to the images in the preprocessing stage.
- Denoise the images using fractional order techniques.
- Train and test the model using the pre-trained architectures (DenseNet121, VGG16, ResNet50, Inception V3).
- Perform data augmentation to remove artifacts and enhance model robustness.
- Performance Metrics:
- Evaluated using precision, recall, F1-score, training accuracy, and testing accuracy.
- Models like DenseNet121 showed significant improvement in performance metrics after applying denoising techniques.
- Training Details
- Optimizer: The Adam optimizer was used with a learning rate of 1 × 10−41\times 10−41 × 10−4.
- Loss Function: Cross-entropy loss for classification tasks or mean squared error (MSE) for regression tasks.
- Batch Size: 32
- Epochs: 50, with early stopping based on validation loss to prevent overfitting.
- Data Augmentation: Techniques such as rotation, flipping, and scaling were applied to the training images to increase the dataset’s diversity and improve the model’s generalization ability.
2.5.2. DenseNet121
2.5.3. VGG16
2.5.4. ResNet50
2.5.5. Inception V3
Algorithm 1: ETLFOD |
Input: Image acquisition from benchmark datasets |
Output: Prediction of the infected region in medical images. |
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3. Experimental Results and Discussion
- Precision
- Recall
- F1-score
- Accuracy
3.1. Results and Discussion of ETLFOD_model for Brain Dataset
3.2. Experimental Analysis of ETLFOD Model for Lung CT Dataset
3.3. Experimental Analysis of ETLFOD Model for Pneumonia Dataset
4. Conclusions and Future Enhancement
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Details | Type of Dataset | Total Images | Malignant/Infected | Benign/Non-Infected | Resolution |
---|---|---|---|---|---|
Brain | MRI | 3762 | 1683 | 2079 | 256 × 256 |
COVID-19 Lung (SARS-CoV-2) | CT | 2074 | 1130 | 944 | 512 × 512 |
Pneumonia (RT-PCR) | X-ray | 5856 | 4273 | 1583 | 512 × 512 |
(With Noise Level σ = 10, α = 1.2) | ||||||
---|---|---|---|---|---|---|
Images | PSNR | MSE | Avg.Time (s) /10 Iterations | PSNR | MSE | Avg.Time (s) /10 iterations |
(By Integer Order MODEL) | (By EFOD Model) | |||||
Brain (MRI) | 26.4821 | 113.39 | 1032 | 41.1533 | 110.85 | 885 |
Lung (CT) | 28.5263 | 61.04 | 968 | 48.8827 | 53.79 | 726 |
Pneumonia (X-ray) | 27.4821 | 68.41 | 904 | 46.3242 | 72.14 | 766 |
Preprocess | Models | Class | Precision | Recall | F1-Score | Training Accuracy | Testing Accuracy |
---|---|---|---|---|---|---|---|
Without Denoising | CNN | 0 | 0.7351 | 0.7000 | 0.7224 | 0.6986 | 0.7214 |
1 | 0.6344 | 0.6840 | 0.6423 | 0.6235 | 0.6923 | ||
DenseNet121 | 0 | 0.8469 | 0.9380 | 0.9153 | 0.9021 | 0.9012 | |
1 | 0.9438 | 0.8722 | 0.8936 | 0.8963 | 0.8935 | ||
VGG16 | 0 | 0.3562 | 0.0032 | 0.0086 | 0.4153 | 0.4123 | |
1 | 0.3125 | 0.8536 | 0.4025 | 0.4235 | 0.4263 | ||
ResNet50 | 0 | 0.8126 | 0.8123 | 0.8365 | 0.8102 | 0.7962 | |
1 | 0.8032 | 0.7965 | 0.7825 | 0.7693 | 0.7922 | ||
Inception V3 | 0 | 0.8235 | 0.8425 | 0.8254 | 0.8125 | 0.7953 | |
1 | 0.8123 | 0.7236 | 0.7856 | 0.7953 | 0.8123 | ||
With Denoising | CNN | 0 | 0.7758 | 0.7500 | 0.7627 | 0.7386 | 0.7719 |
1 | 0.6947 | 0.7242 | 0.7091 | 0.7353 | 0.7500 | ||
DenseNet121 | 0 | 0.9346 | 0.9880 | 0.9606 | 0.9509 | 0.9546 | |
1 | 0.9836 | 0.9121 | 0.9465 | 0.9535 | 0.9544 | ||
VGG16 | 0 | 0.4000 | 0.0047 | 0.0094 | 0.4566 | 0.4486 | |
1 | 0.4389 | 0.9909 | 0.6083 | 0.4978 | 0.4386 | ||
ResNet50 | 0 | 0.8506 | 0.8952 | 0.8723 | 0.8684 | 0.8533 | |
1 | 0.8571 | 0.8000 | 0.8275 | 0.8476 | 0.8533 | ||
Inception V3 | 0 | 0.8397 | 0.8857 | 0.8621 | 0.8669 | 0.8413 | |
1 | 0.8436 | 0.7848 | 0.8131 | 0.8352 | 0.8413 |
Preprocess | Models | Class | Precision | Recall | F1-Score | Training Accuracy | Testing Accuracy |
---|---|---|---|---|---|---|---|
Without Denoising | CNN | 0 | 0.7952 | 0.6000 | 0.6915 | 0.8123 | 0.7123 |
1 | 0.6935 | 0.7136 | 0.7153 | 0.6941 | 0.7164 | ||
DenseNet121 | 0 | 0.8950 | 0.7241 | 0.7952 | 0.9382 | 0.8461 | |
1 | 0.7953 | 0.8951 | 0.8246 | 0.8235 | 0.8125 | ||
VGG16 | 0 | 0.0596 | 0.1203 | 0.2238 | 0.7953 | 0.5947 | |
1 | 0.5523 | 1.5230 | 0.7140 | 0.7247 | 0.5932 | ||
ResNet50 | 0 | 0.8906 | 0.4610 | 0.6124 | 0.8423 | 0.7123 | |
1 | 0.6513 | 0.9123 | 0.7459 | 0.7956 | 0.7214 | ||
Inception V3 | 0 | 0.8935 | 0.5960 | 0.7231 | 0.8961 | 0.8542 | |
1 | 0.7126 | 0.9120 | 0.7956 | 0.7952 | 0.7912 | ||
With Denoising | CNN | 0 | 0.8225 | 0.6500 | 0.7262 | 0.8806 | 0.7545 |
1 | 0.7513 | 0.8833 | 0.8122 | 0.7840 | 0.7666 | ||
DenseNet121 | 0 | 0.9042 | 0.7600 | 0.8267 | 0.9384 | 0.8545 | |
1 | 0.8229 | 0.9333 | 0.8750 | 0.8645 | 0.8641 | ||
VGG16 | 0 | 1.0000 | 0.1700 | 0.2908 | 0.8081 | 0.6227 | |
1 | 0.5913 | 1.0000 | 0.7434 | 0.7967 | 0.6227 | ||
ResNet50 | 0 | 0.9104 | 0.5100 | 0.6536 | 0.9013 | 0.7545 | |
1 | 0.7010 | 0.9583 | 0.8099 | 0.8096 | 0.7964 | ||
Inception V3 | 0 | 0.9535 | 0.6100 | 0.7439 | 0.9121 | 0.8090 | |
1 | 0.7500 | 0.9750 | 0.8478 | 0.8433 | 0.8090 |
Preprocess | Models | Class | Precision | Recall | F1-Score | Training Accuracy | Testing Accuracy |
---|---|---|---|---|---|---|---|
Without Denoising | CNN | 0 | 0.8923 | 0.8213 | 0.7903 | 0.8962 | 0.8823 |
1 | 0.8532 | 0.8952 | 0.8862 | 0.8752 | 0.8825 | ||
DenseNet121 | 0 | 0.9123 | 0.7852 | 0.8563 | 0.8932 | 0.8752 | |
1 | 0.8742 | 0.9236 | 0.8932 | 0.8652 | 0.8825 | ||
VGG16 | 0 | 0.6236 | 0.8236 | 0.7412 | 0.7236 | 0.7923 | |
1 | 0.8752 | 0.6236 | 0.7236 | 0.7921 | 0.7203 | ||
ResNet50 | 0 | 0.7132 | 0.1102 | 0.2234 | 0.7214 | 0.5936 | |
1 | 0.5963 | 0.9536 | 0.6931 | 0.5936 | 0.5532 | ||
Inception V3 | 0 | 0.6325 | 0.9125 | 0.7536 | 0.7512 | 0.7452 | |
1 | 0.9125 | 0.6362 | 0.7963 | 0.7852 | 0.7953 | ||
With Denoising | CNN | 0 | 0.9233 | 0.8656 | 0.8935 | 0.9299 | 0.9130 |
1 | 0.9085 | 0.9488 | 0.9282 | 0.9109 | 0.9142 | ||
DenseNet121 | 0 | 0.9746 | 0.8406 | 0.9026 | 0.9524 | 0.9195 | |
1 | 0.8967 | 0.9844 | 0.9385 | 0.9206 | 0.9246 | ||
VGG16 | 0 | 0.6741 | 0.9518 | 0.7898 | 0.7944 | 0.8287 | |
1 | 0.9520 | 0.6755 | 0.7908 | 0.8137 | 0.7993 | ||
ResNet50 | 0 | 0.7727 | 0.15937 | 0.2642 | 0.7720 | 0.6311 | |
1 | 0.6178 | 0.96666 | 0.7538 | 0.6311 | 0.6311 | ||
Inception V3 | 0 | 0.6941 | 0.97187 | 0.8098 | 0.8103 | 0.8103 | |
1 | 0.9720 | 0.69555 | 0.8108 | 0.8103 | 0.8337 |
Brain Dataset | Lung CT Dataset | Pneumonia Dataset | |||||||
---|---|---|---|---|---|---|---|---|---|
t-statistic | p-value | t-statistic | p-value | t-statistic | p-value | ||||
Paired | CNN | 4.0415 | 0.1544 | CNN | 2.7902 | 0.2191 | CNN | 3.5514 | 0.1747 |
t-test | DenseNet121 | 12.9211 | 0.0492 | DenseNet121 | 2 | 0.2952 | DenseNet121 | 2.1307 | 0.2794 |
results | VGG16 | 1.0078 | 0.4975 | VGG16 | 1.0865 | 0.4736 | VGG16 | 4.8403 | 0.1297 |
ResNet50 | 8.7826 | 0.0722 | ResNet50 | 2.3244 | 0.2586 | ResNet50 | 2.1316 | 0.2793 | |
Inception V3 | 6.9783 | 0.0906 | Inception V3 | 4.3097 | 0.1451 | Inception V3 | 57.6667 | 0.011 | |
chi2-statistic | p-value | chi2-statistic | p-value | t-statistic | p-value | ||||
McNemar’s | CNN | 0.64 | 0.4237 | CNN | 1.0667 | 0.3017 | CNN | 1.0667 | 0.3017 |
test | DenseNet121 | 1.0667 | 0.3017 | DenseNet121 | 4.05 | 0.0442 | DenseNet121 | 4.05 | 0.0442 |
results | VGG16 | 56.7364 | 0 | VGG16 | 52.1524 | 0 | VGG16 | 52.1524 | 0 |
ResNet50 | 0.4571 | 0.499 | ResNet50 | 0.3556 | 0.551 | ResNet50 | 0.3556 | 0.551 | |
Inception V3 | 0.3556 | 0.551 | Inception V3 | 2.7 | 0.1003 | Inception V3 | 2.7 | 0.1003 |
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Annadurai, A.; Sureshkumar, V.; Jaganathan, D.; Dhanasekaran, S. Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning. Fractal Fract. 2024, 8, 511. https://doi.org/10.3390/fractalfract8090511
Annadurai A, Sureshkumar V, Jaganathan D, Dhanasekaran S. Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning. Fractal and Fractional. 2024; 8(9):511. https://doi.org/10.3390/fractalfract8090511
Chicago/Turabian StyleAnnadurai, Abirami, Vidhushavarshini Sureshkumar, Dhayanithi Jaganathan, and Seshathiri Dhanasekaran. 2024. "Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning" Fractal and Fractional 8, no. 9: 511. https://doi.org/10.3390/fractalfract8090511