Abstract
Diabetes is a life-threatening disease that affects various human body organs, including eye retina. Advanced Diabetic Eye disease (DED) leads to permanent vision loss, thus an early detection of DED symptoms is essential to prevent disease escalation and timely treatment. Up till now, research challenges in early DED detection can be summarised as follows: Firstly, changes in the eye anatomy during its early stage are frequently untraceable by human eye due to subtle nature of the features, and Secondly, large volume of fundus images puts a significant strain on limited specialist resources, rendering manual analysis practically infeasible. Thus, Deep Learning-based methods have been practiced to facilitate early DED detection and address the issues currently faced. Despite promising, highly accurate detection of early anatomical changes in the eye using Deep Learning remains a challenge in wide scale practical application. Consequently, in this research we aim to address the main three research gaps and propose the framework for early automated DED detection system on fundus images through Deep Learning.
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Sarki, R., Ahmed, K., Wang, H., Michalska, S., Zhang, Y. (2020). Early Detection of Diabetic Eye Disease from Fundus Images with Deep Learning. In: Borovica-Gajic, R., Qi, J., Wang, W. (eds) Databases Theory and Applications. ADC 2020. Lecture Notes in Computer Science(), vol 12008. Springer, Cham. https://doi.org/10.1007/978-3-030-39469-1_20
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