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Proceedings Paper

Exudate segmentation using fully convolutional neural networks and inception modules
Author(s): Piotr Chudzik; Somshubra Majumdar; Francesco Caliva; Bashir Al-Diri; Andrew Hunter
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Paper 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.

Paper Details

Date Published: 2 March 2018
PDF: 8 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057430 (2 March 2018); doi: 10.1117/12.2293549
Show Author Affiliations
Piotr Chudzik, Univ. of Lincoln (United Kingdom)
Somshubra Majumdar, Univ. of Illinois (United States)
Francesco Caliva, Univ. of Lincoln (United Kingdom)
Bashir Al-Diri, Univ. of Lincoln (United Kingdom)
Andrew Hunter, Univ. of Lincoln (United Kingdom)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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