Diabetic retinopathy detection using red lesion localization and convolutional neural networks

Gabriel Tozatto Zago (1) , Rodrigo Varejão Andreão (1) , Bernadette Dorizzi (2, 3, 4) , Evandro Ottoni Teatini Salles (1)

1 Federal University of Espírito Santo
2 IP Paris - Institut Polytechnique de Paris
3 TSP - EPH - Département Electronique et Physique
4 ARMEDIA-SAMOVAR - ARMEDIA

Detecting the early signs of diabetic retinopathy (DR) is essential, as timely treatment might reduce or even prevent vision loss. Moreover, automatically localizing the regions of the retinal image that might contain lesions can favorably assist specialists in the task of detection. In this study, we designed a lesion localization model using a deep network patch-based approach. Our goal was to reduce the complexity of the model while improving its performance. For this purpose, we designed an efficient procedure (including two convolutional neural network models) for selecting the training patches, such that the challenging examples would be given special attention during the training process. Using the labeling of the region, a DR decision can be given to the initial image, without the need for special training.

Mots clés: Retinal images; Deep learning; Diabetic retinopathy; Convolutional neural networks

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Apprentissage [cs.LG]; Multimédia [cs.MM]; Réseau de neurones [cs.NE]; Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]

Gabriel Tozatto Zago, Rodrigo Varejão Andreão, Bernadette Dorizzi, Evandro Ottoni Teatini Salles. Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Computers in Biology and Medicine, 2020, 116, pp.103537:1-103537:12. ⟨10.1016/j.compbiomed.2019.103537⟩. ⟨hal-03123209⟩