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Platelets Image Classification Through Data Augmentation: A Comparative Study of Traditional Imaging Augmentation and

Itunuoluwa Abidoye¹, Frances Ikeji¹, Charlie A Coupland²

¹Centre of Excellence for Data Science, Artificial Intelligence and Modelling, University of Hull, Hull HU6 7RX, UK.

Journal of Imaging

|June 25, 2025

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Generative Adversarial Networks (GANs) create synthetic platelet images, improving deep learning model performance for disease diagnosis. Combining GANs with traditional augmentation enhances platelet classification accuracy, especially with limited data.

Area of Science:

Medical imaging
Computational pathology
Artificial intelligence in healthcare

Background:

Platelets are vital biomarkers for disease diagnosis and treatment guidance.
Accurate platelet identification and classification are crucial for clinical applications.
Limited availability of diverse platelet image datasets hinders deep learning model development.

Purpose of the Study:

To develop a synthetic platelet image database using Generative Adversarial Networks (GANs).
To evaluate the effectiveness of GAN-generated data compared to traditional augmentation techniques.
To assess the performance of various deep learning models on different dataset sizes and augmentation strategies.

Main Methods:

Generated synthetic platelet images using Wasserstein GAN with Gradient Penalty (WGAN-GP).

Keywords:

CNN GAN WGAN-GP data augmentation medical imaging platelets transfer learning

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Expanded an initial dataset of 71 images to 141 (Level 1) and 1463 (Level 2) using traditional augmentation.

Evaluated eight pre-trained deep learning models and two custom CNNs on augmented and synthetic datasets.

Measured performance using accuracy, precision, recall, and F1-score.

Main Results:

Deep learning models achieved high performance on extensively augmented data (Level 2), with InceptionV3 and InceptionResNetV2 reaching 99% accuracy.
DenseNet201 achieved 98% accuracy on Level 2 data.
GAN-augmented data further boosted DenseNet's performance, indicating its utility in enhancing platelet classification.

Conclusions:

GAN-based data augmentation shows significant potential for improving platelet classification accuracy.
Combining traditional and GAN-based augmentation strategies offers a robust approach for medical imaging tasks.
Synthetic data generation is a promising solution for overcoming data limitations in developing diagnostic AI tools.