Abstract
This paper describes a methodology for diabetic retinopathy detection from eye fundus images using a generalization of the bag-of-visual-words (BoVW) method. We formulate the BoVW as two neural networks that can be trained jointly. Unlike the BoVW, our model is able to learn how to perform feature extraction, feature encoding, and classification guided by the classification error. The model achieves 0.97 area under the curve (AUC) on the DR2 dataset while the standard BoVW approach achieves 0.94 AUC. Also, it performs at the same level of the state-of-the-art on the Messidor dataset with 0.90 AUC.
Highlights
1 Introduction Diabetic retinopathy (DR) is a complication of diabetes mellitus, wherein micro aneurysms start to form in the tiny vessels of the retina
The values for the hyper-parameters were found using random search [16], choosing the values that had the best area under the curve (AUC)
Our method was able to achieve 93% AUC in the DR1 dataset extracting Speeded Up Robust Features (SURF) features
Summary
Diabetic retinopathy (DR) is a complication of diabetes mellitus, wherein micro aneurysms start to form in the tiny vessels of the retina. In later stages of the disease, some retinal blood vessels may become blocked causing vision loss. Patients often do not have symptoms of the disease in its early stages which makes early diagnosis hard. Detection of diabetic retinopathy is paramount for the success of the treatment, as it can prevent up to 98% of severe vision loss [2]. One way of performing the diagnosis of DR is by visually inspecting eye fundus images in order to detect retinal lesions. There are several grades of DR, we are only interested in the task of detecting the disease
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