Detection of exudates and hemorrhages using an efficient criterion for feature selection

Micro-aneurysms, hemorrhages, and hard exudates are the early lesions of diabetic retinopathy, with micro-aneurysms and small hemorrhages appearing as small and multi-size objects in the retinal fundus image. Though it is essentially a promising technique for object classification in images, the deep learning method needs specific investigation for dealing with retinal fundus images. To this, we propose an inception deconvolutional network, dubbed ID-Net, for multi-class object classification. The ID-Net consists of two inception modules, one with a series of convolutional filters used for extracting multi-scale features and the other with a stack of deconvolutional filters for recovering the features and thus increasing the feature representation capability. As a result, the proposed network succeeds in overcoming the under-fitting and representational bottlenecks, two challenges caused by imbalanced data of retinal fundus images. Experimental results show that the ID-Net can accurately classify early lesions, and achieves higher sensitivity and accuracy than the state-of-the-art evaluation on the public database.

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Detection of exudates and hemorrhages using an efficient criterion for feature selection

Cited by 3 publications

References 6 publications

Detection and Classification of Hard Exudates with Fundus Images Complements and Neural Networks

Detection and Classification of Hard Exudates with Fundus Images Complements and Neural Networks

Retinal image segmentation based on weighted local detectors and confusion matrix

Id-Net:inception Deconvolutional Neural Network for Multi-Class Classification in Retinal Fundus Image

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