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Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection
Visual artefacts of early diabetic retinopathy in retinal fundus images are
usually small in size, inconspicuous, and scattered all over retina. Detecting
diabetic retinopathy requires physicians to look at the whole image and fixate
on some specific regions to locate potential biomarkers of the disease.
Therefore, getting inspiration from ophthalmologist, we propose to combine
coarse-grained classifiers that detect discriminating features from the whole
images, with a recent breed of fine-grained classifiers that discover and pay
particular attention to pathologically significant regions. To evaluate the
performance of this proposed ensemble, we used publicly available EyePACS and
Messidor datasets. Extensive experimentation for binary, ternary and quaternary
classification shows that this ensemble largely outperforms individual image
classifiers as well as most of the published works in most training setups for
diabetic retinopathy detection. Furthermore, the performance of fine-grained
classifiers is found notably superior than coarse-grained image classifiers
encouraging the development of task-oriented fine-grained classifiers modelled
after specialist ophthalmologists.Comment: Pages 12, Figures
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