Abstract
Diabetic retinopathy is a highly prevalent disease with a global increase in its occurrence. It is characterized by progressive damage to the retina, the light-sensitive lining at the back of the eye. If left untreated, it can ultimately result in permanent blindness. However, accurately determining the stage of diabetic retinopathy is a complex task that necessitates the expertise of experienced medical professionals. In this study, renowned contemporary architectures such as DenseNet121 and InceptionV3 were adapted and modified to predict diabetic retinopathy stages on the dataset obtained from the Kaggle competition - APTOS 2019 Blindness Detection. An explanation technique was employed to localize regions of distinct lesions to facilitate predictions for ophthalmologists. The findings of this study demonstrate that DenseNet121 outperforms other models, achieving a validation classification accuracy of 83.2%.
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Pham, N.H., Nguyen, H.T. (2023). Diabetic Retinopathy Diagnosis Leveraging Densely Connected Convolutional Networks and Explanation Technique. In: Dao, NN., Thinh, T.N., Nguyen, N.T. (eds) Intelligence of Things: Technologies and Applications. ICIT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-031-46749-3_11
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