  Retinal lesions play a vital role in the accurate classification of retinal
abnormalities. Many researchers have proposed deep lesion-aware screening
systems that analyze and grade the progression of retinopathy. However, to the
best of our knowledge, no literature exploits the tendency of these systems to
generalize across multiple scanner specifications and multi-modal imagery.
Towards this end, this paper presents a detailed evaluation of semantic
segmentation, scene parsing and hybrid deep learning systems for extracting the
retinal lesions such as intra-retinal fluid, sub-retinal fluid, hard exudates,
drusen, and other chorioretinal anomalies from fused fundus and optical
coherence tomography (OCT) imagery. Furthermore, we present a novel strategy
exploiting the transferability of these models across multiple retinal scanner
specifications. A total of 363 fundus and 173,915 OCT scans from seven publicly
available datasets were used in this research (from which 297 fundus and 59,593
OCT scans were used for testing purposes). Overall, a hybrid retinal analysis
and grading network (RAGNet), backboned through ResNet-50, stood first for
extracting the retinal lesions, achieving a mean dice coefficient score of
0.822. Moreover, the complete source code and its documentation are released
at: http://biomisa.org/index.php/downloads/.
