Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and GAN-Based Synthetic Data Generation Techniques Using CNNs
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Description
2.2. Data Preparation and Organization
2.3. Transfer Learning Models
- Custom Model 1: Incorporating Conv2D, BatchNormalization, MaxPooling, Dropout, and two Dense layers.
- Custom Model 2: A simpler architecture with Conv2D and MaxPooling, followed by a single Dense layer.
2.4. Data Augmentation (GAN Data Generation)
- Generator: This network transforms a latent noise vector into high-resolution (128 × 128 or 256 × 256 pixels) synthetic images using transpose convolutions, batch normalization, and LeakyReLU activations to ensure realistic feature generation.
- Critic (Discriminator): The discriminator evaluates both real and generated images through convolutional layers, outputting a scalar “realness” score to guide the generator’s improvement. To maintain training stability, the critic undergoes multiple updates per generator update before reaching convergence.
2.5. Model Training and Evaluation
2.5.1. Model Training
- Optimizer: Adam with a learning rate of 0.001.
- Batch Size: 32 (128 tested in some trials).
- Loss Function: Categorical cross-entropy for the multi-class classification.
2.5.2. Evaluation Metrics
3. Results
3.1. Original Dataset (71 Images)
Models (Batch Size 32) | Accuracy (%) | F1-Score (%) | Precision (%) | Recall (%) |
---|---|---|---|---|
Custom Model 1 | 62 | 56 | 68 | 62 |
Custom Model 2 | 57 | 42 | 33 | 57 |
DenseNet121 | 81 | 79 | 84 | 81 |
DenseNet169 | 71 | 67 | 77 | 71 |
DenseNet201 | 76 | 74 | 83 | 76 |
VGG16 | 57 | 47 | 43 | 57 |
VGG19 | 52 | 45 | 40 | 52 |
VGG19-FF | 62 | 59 | 63 | 62 |
InceptionV3 | 62 | 51 | 46 | 62 |
InceptionResNetV2 | 71 | 69 | 76 | 71 |
AlexNet | 62 | 56 | 59 | 62 |
3.2. Augmented Dataset Level 1 (141 Images)
3.3. Augmented Dataset Level 2 (1463 Images)
3.4. GAN-Augmented Dataset (300 Images)
3.5. Synthetic Dataset Result
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
CNNs | Convolutional Neural Networks |
GAN | Generative Adversarial Network |
WGAN-GP | Wasserstein GAN with Gradient Penalty |
FID | Fréchet Inception Distance |
IS | Inception Score |
FF | Fine-Tuning |
ReLU | Rectified Linear Unit |
TPUs | Tensor Processing Units |
GPUs | Graphics Processing Units |
cGAN | Conditional Generative Adversarial Network |
CycleGAN | Cycle-Consistent Generative Adversarial Network |
MRI | Magnetic Resonance Imaging |
TP | True Positive |
FP | False Positive |
TN | True Negative |
FN | False Negative |
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Models (Batch Size 32) | Accuracy (%) | F1-Score (%) | Precision (%) | Recall (%) |
---|---|---|---|---|
Custom Model 1 | 67 | 66 | 69 | 67 |
Custom Model 2 | 38 | 21 | 15 | 38 |
DenseNet121 | 79 | 79 | 79 | 79 |
DenseNet169 | 79 | 78 | 83 | 79 |
DenseNet201 | 86 | 86 | 88 | 86 |
VGG16 | 62 | 62 | 72 | 62 |
VGG19 | 64 | 64 | 68 | 64 |
VGG19-FF | 76 | 76 | 80 | 76 |
InceptionV3 | 76 | 76 | 79 | 76 |
InceptionResNetV2 | 71 | 69 | 80 | 71 |
AlexNet | 67 | 65 | 66 | 67 |
Models (Batch Size 32) | Accuracy (%) | F1-Score (%) | Precision (%) | Recall (%) |
---|---|---|---|---|
Custom Model 1 | 97 | 97 | 97 | 97 |
Custom Model 2 | 88 | 87 | 91 | 88 |
DenseNet121 | 97 | 97 | 98 | 97 |
DenseNet169 | 97 | 97 | 97 | 97 |
DenseNet201 | 98 | 98 | 98 | 98 |
VGG16 | 97 | 97 | 97 | 97 |
VGG19 | 94 | 94 | 94 | 94 |
VGG19-FF | 95 | 95 | 95 | 95 |
InceptionV3 | 99 | 99 | 99 | 99 |
InceptionResNetV2 | 99 | 99 | 99 | 99 |
AlexNet | 30 | 14 | 9 | 30 |
Models (Batch Size 32) | Accuracy (%) | F1-Score (%) | Precision (%) | Recall (%) |
---|---|---|---|---|
Custom Model 1 | 97 | 94 | 95 | 94 |
Custom Model 2 | 87 | 87 | 87 | 87 |
DenseNet121 | 97 | 97 | 97 | 97 |
DenseNet169 | 91 | 91 | 93 | 91 |
DenseNet201 | 96 | 96 | 96 | 96 |
VGG16 | 83 | 83 | 85 | 83 |
VGG19 | 89 | 89 | 89 | 89 |
VGG19-FF | 88 | 88 | 89 | 88 |
InceptionV3 | 94 | 94 | 95 | 94 |
InceptionResNetV2 | 90 | 90 | 90 | 90 |
AlexNet | 74 | 75 | 75 | 74 |
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Abidoye, I.; Ikeji, F.; Coupland, C.A.; Calaminus, S.D.J.; Sander, N.; Sousa, E. Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and GAN-Based Synthetic Data Generation Techniques Using CNNs. J. Imaging 2025, 11, 183. https://doi.org/10.3390/jimaging11060183
Abidoye I, Ikeji F, Coupland CA, Calaminus SDJ, Sander N, Sousa E. Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and GAN-Based Synthetic Data Generation Techniques Using CNNs. Journal of Imaging. 2025; 11(6):183. https://doi.org/10.3390/jimaging11060183
Chicago/Turabian StyleAbidoye, Itunuoluwa, Frances Ikeji, Charlie A. Coupland, Simon D. J. Calaminus, Nick Sander, and Eva Sousa. 2025. "Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and GAN-Based Synthetic Data Generation Techniques Using CNNs" Journal of Imaging 11, no. 6: 183. https://doi.org/10.3390/jimaging11060183
APA StyleAbidoye, I., Ikeji, F., Coupland, C. A., Calaminus, S. D. J., Sander, N., & Sousa, E. (2025). Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and GAN-Based Synthetic Data Generation Techniques Using CNNs. Journal of Imaging, 11(6), 183. https://doi.org/10.3390/jimaging11060183