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Since the beginning of this decade, CNN has been a very successful tool inthe field of Computer Vision tasks.The invention of CNN was inspired fromneuroscience and it shares a lot of anatomical similarities with our visualsystem.Inspired by the anatomyof humanvisual system, wearguethat the existingU-Net architecture can be improvedin many ways. As human visual system usesattention mechanism, we have used attention concatenation in place ofnormalconcatenation.Although, CNN is purely feed-forward in nature butanatomical evidences show that our brain contains recurrent synapses and theyoften outnumber feed-forward and top-down connections. Thisfact inspiresus touserecurrent convolution connectionsin place of normalconvolution blocksinU-Net.Thispaper also addressesthe class imbalance issuein the field of medicalimage analysis. The paperresolvestheproblem of class imbalanceswith the help ofstate-of-the-art loss functions.Weargue thatourproposed architecturecan betrained end to end with a few training data and it outperforms the othervariantsof U-Net.

Convolutional Recurrent Residual U-Net Embedded with Attention Mechanism and Focal Tversky Loss Function for Cancerous Nuclei Detection