AI-Based Aortic Stenosis Classification in MRI Scans
Abstract
:1. Introduction
2. State of the Art
3. Methods
3.1. Business and Data Understanding
3.2. Data Preparation
- We used rotational augmentation to rotate photos at 90-, 180-, and 270-degree angles, depicted in Figure 4. This geometric modification not only increased the size of our dataset by 3 times but also created useful variations in orientation, increasing the information available to our models. We only intended to spin the MRIs at four angles because rotating an MRI to a random degree between those mentioned above would not be realistic because the patient is not in a 15 degree position during the exam, for example. This creates 606 new MRIs out of the original dataset.
- 2.
- We executed translation along the x-axis (Figure 5) while meticulously ensuring that the aortic valve remained within the frame; with this, we created 202 more MRIs out of the original dataset.
- 3.
- We applied horizontal flipping (Figure 6), further diversifying our dataset by creating mirrored counterparts of existing images. With this process, we created 202 more MRIs and also introduced new perspectives for our models to learn from.
- 4.
- Recognizing by the cardiology specialist the occasional presence of underexposed images, we addressed this issue by enhancing brightness in the images (Figure 7). By compensating for the darker images, we ensured that our dataset covered a wider spectrum of lighting conditions, thus reinforcing the adaptability of our models. With this technique, we created 404 more MRIs.
- 1st test (without data augmentation)—202 images, 91 with calcification and 111 without calcification.
- 2nd test (rotation, flip, and translation)—1212 images, 546 with calcification and 666 without calcification.
- 3rd test (rotation, flip, translation, and brightness)—1616 pictures, 729 with calcification and 888 without calcification.
3.3. Modeling
4. Evaluation and Discussion
Evaluation of the Models
- Input shape was defined based on the architecture of each model, where on VGG16 and ResNet50 was (224, 224, 3) and for the Xception was (299, 299, 3).
- The number of batches was set to 32 based on the following Formula (1), where N is the number of samples divided with B the batch size multiplied by E number of epochs [65].
- The number of epochs was set to 30 based on a considerable number of tests. Initially, we began with 10 epochs, but, through experimentation, we observed that the model could be effectively trained for additional epochs without compromising the results. As we increased the number of epochs, we found that not a single test could reach 30 epochs. This was due to the implementation of the early stopping function, indicating that the models were reaching their full capacity. Our early stopping function was defined with a ‘patience’ parameter set to 4. This means that if, during training, we did not see better results for four consecutive epochs, the model would stop. This approach was implemented to reduce overfitting while preserving model performance, ultimately saving both time and computational resources.
- The first test was with the original dataset (without data augmentation) containing 202 MRIs, 91 with calcification and 111 without calcification.
- In the second test, we applied flip, rotation, and translation techniques, ending up with 1212 MRIs, 546 with calcification and 666 without calcification.
- In the third and final test, we added images to the dataset created in the second test using an extra technique known as brightness. The collection now has 1616 MRIs, 729 with calcification and 888 without calcification.
5. Conclusions
Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Topic | References | # Doc | % Doc |
---|---|---|---|
Aortic Disease/Aortic Stenosis | [1,2,3,5,6,8,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35] | 32 | 21% |
MRI | [1,2,3,6,8,10,11,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,36,37,38,39,40] | 31 | 20% |
Artificial Intelligence | [1,3,6,11,13,14,18,20,26,30,32,35,36,38,41,42,43,44,45,46] | 20 | 13% |
Tomography Scan | [6,11,12,14,17,18,21,23,24,34,36,37,39,41] | 14 | 9% |
Echocardiography | [1,6,21,27,34,35,36,39] | 8 | 5% |
Early Detection/Prevention | [5,10,13,19,20,36,47] | 7 | 4% |
Test | Accuracy | Recall | Precision | F1-Score |
---|---|---|---|---|
| 0.77 | 0.77 | 0.77 | 0.77 |
| 0.78 | 0.78 | 0.78 | 0.78 |
| 0.81 | 0.81 | 0.81 | 0.81 |
Test | Models | Recall | Precision | F1-Score |
---|---|---|---|---|
1. Original Dataset | VGG16 | 0.5 | 0.5 | 0.65 |
VGG16-FT | 0.75 | 0.88 | 0.81 | |
ResNet50 | 0.6 | 0.86 | 0.71 | |
ResNet50-FT | 0.8 | 0.94 | 0.86 | |
Xception | 0.85 | 0.85 | 0.85 | |
Xception-FT | 0.546 | 0.586 | 0.565 | |
2. Rotation, Flip, and Translation | VGG16 | 0.9 | 0.92 | 0.91 |
VGG16-FT | 0.85 | 0.96 | 0.9 | |
ResNet50 | 0.88 | 0.97 | 0.93 | |
ResNet50-FT | 0.93 | 1 | 0.96 | |
Xception | 0.86 | 0.86 | 0.86 | |
Xception-FT | 0.73 | 0.87 | 0.8 | |
3. Rotation, Flip, Translation, and Brightness | VGG16 | 0.95 | 0.96 | 0.95 |
VGG16-FT | 0.85 | 0.98 | 0.91 | |
ResNet50 | 0.82 | 0.95 | 0.88 | |
ResNet50-FT | 0.89 | 0.96 | 0.92 | |
Xception | 0.86 | 0.86 | 0.86 | |
Xception-FT | 0.64 | 0.87 | 0.74 |
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Elvas, L.B.; Águas, P.; Ferreira, J.C.; Oliveira, J.P.; Dias, M.S.; Rosário, L.B. AI-Based Aortic Stenosis Classification in MRI Scans. Electronics 2023, 12, 4835. https://doi.org/10.3390/electronics12234835
Elvas LB, Águas P, Ferreira JC, Oliveira JP, Dias MS, Rosário LB. AI-Based Aortic Stenosis Classification in MRI Scans. Electronics. 2023; 12(23):4835. https://doi.org/10.3390/electronics12234835
Chicago/Turabian StyleElvas, Luís B., Pedro Águas, Joao C. Ferreira, João Pedro Oliveira, Miguel Sales Dias, and Luís Brás Rosário. 2023. "AI-Based Aortic Stenosis Classification in MRI Scans" Electronics 12, no. 23: 4835. https://doi.org/10.3390/electronics12234835
APA StyleElvas, L. B., Águas, P., Ferreira, J. C., Oliveira, J. P., Dias, M. S., & Rosário, L. B. (2023). AI-Based Aortic Stenosis Classification in MRI Scans. Electronics, 12(23), 4835. https://doi.org/10.3390/electronics12234835