  Every year millions of people die due to disease of Cancer. Due to its
invasive nature it is very complex to cure even in primary stages. Hence, only
method to survive this disease completely is via forecasting by analyzing the
early mutation in cells of the patient biopsy. Cell Segmentation can be used to
find cell which have left their nuclei. This enables faster cure and high rate
of survival. Cell counting is a hard, yet tedious task that would greatly
benefit from automation. To accomplish this task, segmentation of cells need to
be accurate. In this paper, we have improved the learning of training data by
our network. It can annotate precise masks on test data. we examine the
strength of activation functions in medical image segmentation task by
improving learning rates by our proposed Carving Technique. Identifying the
cells nuclei is the starting point for most analyses, identifying nuclei allows
researchers to identify each individual cell in a sample, and by measuring how
cells react to various treatments, the researcher can understand the underlying
biological processes at work. Experimental results shows the efficiency of the
proposed work.
