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001-es BibID:BIBFORM101999
Első szerző:Harangi Balázs (programtervező matematikus)
Cím:Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features / Harangi Balázs, Tóth János, Baran Ágnes, Hajdu András
Dátum:2019
Megjegyzések:Diabetic retinopathy (DR) and especially diabetic macular edema (DME) are common causes of vision loss as complications of diabetes. In this work, we consider an ensemble that organizes a convolutional neural network (CNN) and traditional hand-crafted features into a single architecture for retinal image classification. This approach allows the joint training of a CNN and the fine-tuning of the weights of handcrafted features to provide a final prediction. Our solution is dedicated to the automatic classification of fundus images according to the severity level of DR and DME. For an objective evaluation of our approach, we have tested its performance on the official test datasets of the IEEE International Symposium on Biomedical Imaging (ISBI) 2018 Challenge 2: Diabetic Retinopathy Segmentation and Grading Challenge, section B. Disease Grading: Classification of fundus images according to the severity level of diabetic retinopathy and diabetic macular edema. As for our experimental results based on testing on the Indian Diabetic Retinopathy Image Dataset (IDRiD), the classification accuracies have been found to be 90.07% for the 5-class DR challenge, and 96.85% for the 3-class DME one.
ISBN:9781538613122
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
diabetic retinopathy screening
hand-crafted features
deep learning
ensemble learning
Megjelenés:41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / ed. Riccardo Barbieri. - p. 2699-2702. -
További szerzők:Tóth János (1984-) (programtervező matematikus) Baran Ágnes (1972-) (matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:EFOP-3.6.2-16-2017-00015
EFOP
EFOP-3.6.3-VEKOP-16-2017-00002
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