An Efficient Deep Neural Framework for Nucleus Semantic Segmentation with Enhanced U-Net

Abstract

With the advancement of medicine, there is a terrific requirement to process an increasing number of medical imaging where image segmentation comes into play. Single cell division is generally among the first as well as most essential tasks in image-based cell analysis. The identification of the nuclei allows pathologists to determine each cell in the sample, and by measuring how cells respond to various treatments, they can comprehend the fundamental biological processes in work. In this paper we gathered a deep learning network which detects and parts the cell microscopy image. Highly advanced performance is achieved in image segmentation tasks through deep learning-based techniques. These procedures are complex and need the support of compelling computational resources. This paper emphasizes the basic principles of the methods used to segment an image. We have implemented U-Net for the semantic segmentation of nucleus. A successful implementation will aid researchers immensely in their fight to find pharmaceutical solutions to medical crises while saving both valuable research time and funding.