Effective diabetic retinopathy classification with Siamese Neural Network: A strategy for small dataset challenges

Tariq, Maria, Palade, Vasile and Ma, YingLiang ORCID: https://orcid.org/0000-0001-5770-5843 (2024) Effective diabetic retinopathy classification with Siamese Neural Network: A strategy for small dataset challenges. IEEE Access, 12. pp. 182814-182827. ISSN 2169-3536

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Abstract

Early detection of diabetic retinopathy, a complication of vision loss in advanced stages of diabetes, is essential to avoid permanent blindness. However, the automatic detection of diabetic retinopathy through medical image processing requires a large number of training data to build a model with good performance. This poses a challenge when working with small datasets as these models need large datasets to perform well on unseen data. In this paper, we design a few-shot Siamese Neural Networks combined with pre-trained models, such as VGG16, ResNet50, and DenseNet121, to effectively differentiate between classes using small lesions in the retinal images. The proposed model is trained based on the similarity between the pair of images using a comparatively small dataset and performs well for a five-class classification problem. We use the Fine-Grained Annotated Diabetic Retinopathy (FGADR) and APTOS 2019 Blindness Detection dataset, where a small ratio of training images is used to train the model. To evaluate our model, we conduct the testing on the remaining data and achieve good accuracy when trained on limited images, with fewer epochs and fewer parameters. The proposed model achieves high accuracy rates on five-class classification of 80% on FGADR and 81% on APTOS 2019 datasets, with a consistent quadratic weighted kappa (QWK) score of 0.89 across both datasets. Furthermore, we conduct an in-depth analysis of hyperparameter optimisation, specifically investigating different pair selection techniques, loss functions, and distance layers to thoroughly evaluate their impact on the performance of the model. Our proposed model demonstrates promising results when combined with an attention mechanism to perform multiclass classification of diabetic retinopathy using a limited number of eye fundus images, outperforming existing approaches with only a small number of epochs in training.

Item Type:	Article
Additional Information:	Funding information: 10.13039/501100000266-Engineering and Physical Sciences Research Council (Grant Number: EP/X023826/1)
Uncontrolled Keywords:	sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Data Science and AI
Depositing User:	LivePure Connector
Date Deposited:	10 Dec 2024 01:45
Last Modified:	16 Dec 2024 01:43
URI:	https://ueaeprints.uea.ac.uk/id/eprint/97954
DOI:	10.1109/ACCESS.2024.3510556

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