  Since the beginning of this decade, CNN has been a very successful tool in
the field of Computer Vision tasks.The invention of CNN was inspired from
neuroscience and it shares a lot of anatomical similarities with our visual
system.Inspired by the anatomyof humanvisual system, wearguethat the existing
U-Net architecture can be improvedin many ways. As human visual system uses
attention mechanism, we have used attention concatenation in place of
normalconcatenation.Although, CNN is purely feed-forward in nature but
anatomical evidences show that our brain contains recurrent synapses and they
often outnumber feed-forward and top-down connections. Thisfact inspiresus to
userecurrent convolution connectionsin place of normalconvolution blocksin
U-Net.Thispaper also addressesthe class imbalance issuein the field of medical
image analysis. The paperresolvestheproblem of class imbalanceswith the help of
state-of-the-art loss functions.Weargue thatourproposed architecturecan be
trained end to end with a few training data and it outperforms the other
variantsof U-Net.
