Abstract
Diabetic Retinopathy (DR) is an ocular complication of diabetes that leads to a significant loss of vision. Screening retinal fundus images allows ophthalmologists to early detect and diagnose this disease; however, the manual interpretation of images is a time-consuming task. Deep image classification models deal with this drawback and provide an efficient method to diagnose DR, but they are mainly trained with balanced dataset were all stages of DR are equally represented—a scenario that does not reflect the reality during screening. In this work, we have conducted a study of Deep Learning models to rate the severity of Diabetic Retinopathy from digital fundus images when working with a highly imbalanced dataset. Our approach, that is based on the ensemble of several ResNetRS models, achieves an agreement rate with the specialists above that of primary care physicians (\(\kappa \) of 0.81 vs. 0.75). In addition, our method can also be applied in a binary fashion to determine whether a case is derivable or non-derivable; achieving again an agreement with the specialists above that of primary care physicians (\(\kappa \) of 0.76 vs. 0.64).
This work was partially supported by Ministerio de Ciencia e Innovación [PID2020-115225RB-I00/AEI/10.13039/501100011033]. Ángela Casado-García has a FPI grant from Community of La Rioja 2020. Manuel García-Domínguez has a FPI grant from Community of La Rioja 2018. Adrián Inés has a FPU Grant [16/06903] of the Spanish Ministerio de Educación y Ciencia.
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Casado-García, Á., García-Domínguez, M., Heras, J., Inés, A., Royo, D., Zapata, M.Á. (2022). Rating the Severity of Diabetic Retinopathy on a Highly Imbalanced Dataset. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_52
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