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Publicly available diabetic retinopathy (DR) datasets are imbalanced,
containing limited numbers of images with DR. This imbalance contributes to
overfitting when training machine learning classifiers. The impact of this
imbalance is exacerbated as the severity of the DR stage increases, affecting
the classifiers' diagnostic capacity. The imbalance can be addressed using
Generative Adversarial Networks (GANs) to augment the datasets with synthetic
images. Generating synthetic images is advantageous if high-quality and
diversified images are produced. To evaluate the quality and diversity of
synthetic images, several evaluation metrics, such as Multi-Scale Structural
Similarity Index (MS-SSIM), Cosine Distance (CD), and Fr\'echet Inception
Distance (FID) are used. Understanding the effectiveness of each metric in
evaluating the quality and diversity of GAN-based synthetic images is critical
to select images for augmentation. To date, there has been limited analysis of
the appropriateness of these metrics in the context of biomedical imagery. This
work contributes an empirical assessment of these evaluation metrics as applied
to synthetic Proliferative DR imagery generated by a Deep Convolutional GAN
(DCGAN). Furthermore, the metrics' capacity to indicate the quality and
diversity of synthetic images and a correlation with classifier performance is
undertaken. This enables a quantitative selection of synthetic imagery and an
informed augmentation strategy. Results indicate that FID is suitable for
evaluating the quality, while MS-SSIM and CD are suitable for evaluating the
diversity of synthetic imagery. Furthermore, the superior performance of
Convolutional Neural Network (CNN) and EfficientNet classifiers, as indicated
by the F1 and AUC scores, for the augmented datasets demonstrates the efficacy
of synthetic imagery to augment the imbalanced dataset.

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