The process of identifying and segmenting nuclei in the cell is a prerequisite for the analysis of various genetic disorders. The main carrier of genetic information in most of the living organisms is Deoxyribonucleic acid(DNA) which is present in the nucleus of the cell. Detection and segmentation of nuclei is laborious and time demanding. This paper intends to explore an untouched approach towards solving the issue by automating the process which drastically reduces the development time and required man power. Many classic methods like Otsu, watershed were proposed but they failed to accurately segment and few caused over segmentation. In the recent timespan, the executions of Convolutional Neural Networks (CNN) have made it evident that they demonstrate impressive performance on biomedical image classification. CNN methods also face issues with stipulation for hefty delineated tutoring data sets but in this context, a CNN architecture U-Net which is proficient of grasping knowledge from smaller pre-processed augmented data-set is proposed. The proposed encoder-decoder U-Net model indicates better execution in identifying genuine fragments contrasted with the cutting edge system for rapid CNN shows better performance in detecting true segments compared to the state-of-the-art technique Faster Recurrent-CNN (R-CNN).

CNN, R-CNN, U-Net Model

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