  Diabetic retinopathy (DR) is the most common form of diabetic eye disease.
Retinopathy can affect all diabetic patients and becomes particularly
dangerous, increasing the risk of blindness, if it is left untreated. The
success rate of its curability solemnly depends on diagnosis at an early stage.
The development of automated computer aided disease diagnosis tools could help
in faster detection of symptoms with a wider reach and reasonable cost. This
paper proposes a method for the automated segmentation of retinal lesions and
optic disk in fundus images using a deep fully convolutional neural network for
semantic segmentation. This trainable segmentation pipeline consists of an
encoder network, a corresponding decoder network followed by pixel-wise
classification to segment microaneurysms, hemorrhages, hard exudates, soft
exudates, optic disk from background. The network was trained using Binary
cross entropy criterion with Sigmoid as the last layer, while during an
additional SoftMax layer was used for boosting response of single class. The
performance of the proposed method is evaluated using sensitivity, positive
prediction value (PPV) and accuracy as the metrices. Further, the position of
the Optic disk is localised using the segmented output map.
