Nuclei Detection for Drug Discovery using Deep Learning
Nishat Sayyed1, Vidit Patil2, Mohammed Painter3, Deepali Nayak4
1Nishat Sayyed, Final Year Students, Department of Information Technology, Vidyalankar Institute of Technology, University of Mumbai (Maharastra), India.
2Vidit Patil, Final Year Students, Department of Information Technology, Vidyalankar Institute of Technology, University of Mumbai (Maharastra), India.
3Mohammed Painter, Final Year Students, Department of Information Technology, Vidyalankar Institute of Technology, University of Mumbai (Maharastra), India.
4Prof. Deepali Nayak, Final Year Students, Department of Information Technology, Vidyalankar Institute of Technology, University of Mumbai (Maharastra), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1289-1294 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10550882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1055.0882S819
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Identifying a cell’s nucleus is the starting point for analysis of any kind of drug research. Presently this process is manually carried out by scientists. They take note of each nucleus from microscopic images to begin the drug discovery process. This takes hundreds of thousands of hours for scientific researchers to get their job done. In order to avoid such a bottleneck, this paper proposes an efficient solution using machine learning/ deep learning model. The proposed system can spot nuclei in cell images along with its run-length-encoded code without biologist intervention. A U-Net framework is used for the training the model to create efficient system. GPU based system is implemented to get accurate results for storage, retrieval and training of medical cell images. Thus, the system automates the spotting of nuclei thereby drastically reducing time in the drug discovery process.
Keywords: Microscopy-Images, Nuclei, Segmentation, U-Net, Deep-Learning.
Scope of the Article: Deep Learning