Automatic Identification of Glaucoma Using Deep Learning Methods
This paper proposes an automatic classification method to detect glaucoma in fundus images. The method is based on training a neural network using public image databases. The network used in this paper is the GoogLeNet, adapted for this proposal. The methodology was divided into two stages, namely: (1) detection of the region of interest (ROI); (2) image classification. We first used a sliding-window approach combined with the GoogLeNet network. This network was trained using manually extracted ROIs and other fundus image structures. Afterwards, another GoogLeNet model was trained using the previous resulting images. Then those images were used to train another GoogLeNet model to automatically detect glaucoma. To prevent overfitting, data augmentation techniques were used on smaller databases. The results demonstrated that the network had a good accuracy, even with poor quality images found in some databases or generated by the data augmentation algorithm.
Glaucoma Retina Neural Network (Computer)
Allan Cerentini Daniel Welfer Marcos Cordeiro dOrnellas Carlos Jesus Pereira Haygert Gustavo Nogara Dotto
Graduate Program in Computer Science(PPGI),Department of Applied Computing (DCOM),Santa Maria,Rio Gr Department of Clinical Medicine,Santa Maria University Hospital,Federal University of Santa Maria (U
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
318-321
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）