DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis-Reference-Cited by-同舟云学术

DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis

Published:2022-11 Issue:11 Volume:26 Page:5575-5583
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Eren Furkan¹,Aslan Mete²,Kanarya Dilek³,Uysalli Yigit⁴,Aydin Musa⁵,Kiraz Berna¹^ORCID,Aydin Omer⁶^ORCID,Kiraz Alper⁴^ORCID

Affiliation:

1. Optofil, Inc., Istanbul, Türkiye

2. Department of Physics, Koç University, Istanbul, Türkiye

3. Department of Biomedical Engineering, and NanoThera Lab, ERFARMA-Drug Application and Research Center, Erciyes University, Kayseri, Türkiye

4. Optofil, Inc., Istanbul, Turkey

5. Department of Computer Engineering, Fatih Sultan Mehmet Vakif University, Istanbul, Türkiye

6. Department of Biomedical Engineering, NanoThera Lab, ERFARMA-Drug Application and Research Center, and ERNAM-Nanotechnology Research and Application Center, Erciyes University, Kayseri, Türkiye

Funder

Turkish Academy of Sciences

TÜTAK-TEYDEB

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/9945616/09875013.pdf?arnumber=9875013

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