  Retinal vascular segmentation, is a widely researched subject in biomedical
image processing, aims to relieve ophthalmologists' workload when treating and
detecting retinal disorders. However, segmenting retinal vessels has its own
set of challenges, with prior techniques failing to generate adequate results
when segmenting branches and microvascular structures. The neural network
approaches used recently are characterized by the inability to keep local and
global properties together and the failure to capture tiny end vessels make it
challenging to attain the desired result. To reduce this retinal vessel
segmentation problem, we propose a full-scale micro-vessel extraction mechanism
based on an encoder-decoder neural network architecture, sigmoid smoothing, and
an adaptive threshold method. The network consists of of residual, encoder
booster, bottleneck enhancement, squeeze, and excitation building blocks. All
of these blocks together help to improve the feature extraction and prediction
of the segmentation map. The proposed solution has been evaluated using the
DRIVE, CHASE-DB1, and STARE datasets, and competitive results are obtained when
compared with previous studies. The AUC and accuracy on the DRIVE dataset are
0.9884 and 0.9702, respectively. On the CHASE-DB1 dataset, the scores are
0.9903 and 0.9755, respectively. On the STARE dataset, the scores are 0.9916
and 0.9750, respectively. The performance achieved is one step ahead of what
has been done in previous studies, and this results in a higher chance of
having this solution in real-life diagnostic centers that seek ophthalmologists
attention.
