Nuclei detection for drug discovery using deep learning

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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.




Sayyed, N., Patil, V., Painter, M., & Nayak, D. (2019). Nuclei detection for drug discovery using deep learning. International Journal of Recent Technology and Engineering, 8(2 Special Issue 8), 1289–1294.

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