  Diabetic retinopathy is one of the leading causes of preventable blindness in
the world. Its earliest sign are red lesions, a general term that groups both
microaneurysms and hemorrhages. In daily clinical practice, these lesions are
manually detected by physicians using fundus photographs. However, this task is
tedious and time consuming, and requires an intensive effort due to the small
size of the lesions and their lack of contrast. Computer-assisted diagnosis of
DR based on red lesion detection is being actively explored due to its
improvement effects both in clinicians consistency and accuracy. Several
methods for detecting red lesions have been proposed in the literature, most of
them based on characterizing lesion candidates using hand crafted features, and
classifying them into true or false positive detections. Deep learning based
approaches, by contrast, are scarce in this domain due to the high expense of
annotating the lesions manually. In this paper we propose a novel method for
red lesion detection based on combining both deep learned and domain knowledge.
Features learned by a CNN are augmented by incorporating hand crafted features.
Such ensemble vector of descriptors is used afterwards to identify true lesion
candidates using a Random Forest classifier. We empirically observed that
combining both sources of information significantly improve results with
respect to using each approach separately. Furthermore, our method reported the
highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for
screening and need for referral on MESSIDOR compared to a second human expert.
Results highlight the fact that integrating manually engineered approaches with
deep learned features is relevant to improve results when the networks are
trained from lesion-level annotated data. An open source implementation of our
system is publicly available online.
