Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/67756
Title: Detection of hard exudates in fundus images using convolutional neural networks
Authors: Ittided Poonkasem
Nipon Theera-Umpon
Sansanee Auephanwiriyakul
Direk Patikulsila
Keywords: Computer Science
Decision Sciences
Energy
Physics and Astronomy
Issue Date: 1-Jan-2019
Abstract: © 2019 IEEE. The patients with diabetes have a chance to have blindness. An impairment of metabolism can cause a high glucose level in blood vessel leading to an abnormality called hard exudates. Hard exudates are often arranged in clumps or circinate rings and located in the outer layer of the retina. The aim of this research is to detect hard exudates by applying image processing techniques and classify them by using convolutional neuron network (CNN). DIARETDB1 dataset is used in the experiments. The proposed method achieves the area under the curve (AUC) of 0.97 and 0.95 on the training and validation sets, respectively, of 10-fold cross validation experiment. These show that the combination of image processing techniques, three channels of fundus images, and CNN can perform as a promising classification tool in hard exudates detection system.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85074277142&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/67756
Appears in Collections:CMUL: Journal Articles

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