Abstract

Diabetic retinopathy is an eye disease associated with diabetes mellitus and also it is the leading cause of
preventable blindness in working-age population. Early detection and treatment of DR is essential to prevent
vision loss. Exudates are one of the earliest signs of diabetic retinopathy. This paper proposes an automatic
method for the detection and segmentation of exudates in fundus photographies. A novel fully convolutional
neural network architecture with Inception modules is proposed. Compared to other methods it does not require
the removal of other anatomical structures. Furthermore, a transfer learning approach is applied between small
datasets of different modalities from the same domain. To the best of authors’ knowledge, it is the first time
that such approach has been used in the exudate segmentation domain. The proposed method was evaluated
using publicly available E-Ophtha datasets. It achieved better results than the state-of-the-art methods in terms
of sensitivity and specificity metrics. The proposed algorithm accomplished better results using a diseased/not
diseased evaluation scenario which indicates its applicability for screening purposes. Simplicity, performance,
efficiency and robustness of the proposed method demonstrate its suitability for diabetic retinopathy screening
applications.