Revolutionizing Live-Cell Tracking with Machine Learning: Bridging the Gap Between Automation and Precision in Quantitative Biology-Reference-Cited by-同舟云学术

Revolutionizing Live-Cell Tracking with Machine Learning: Bridging the Gap Between Automation and Precision in Quantitative Biology

Published:2023-11-14 Issue: Volume: Page:287-292
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Container-title:2023 International Conference for Technological Engineering and its Applications in Sustainable Development (ICTEASD)
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Author:

Sapaev I. B.¹,Saxena Archana²,Chaturvedi Rishabh³,Alzubaidi Laith H.⁴,Nigam Nishesh⁵,Valarmathy A. S.⁶,Surekha Pothireddy⁷

Affiliation:

1. National Research University,Tashkent Institute of Irrigation and Agricultural Mechanization Engineers,Tashkent,Uzbekistan

2. Uttaranchal Institute of Management Uttaranchal University,Department of Management,Dehradun,Uttarakhand,India

3. GLA University,Department of Mechanical Engineering,Mathura

4. The Islamic University,Najaf,Iraq

5. IES University,Computer Science & Engineering IES Institute of Technology and Management,Bhopal,Madhya Pradesh,India,462044

6. Prince Shri Venkateshwara Padmavathy Engineering College PSVPEC,Electrical and Electronics Engineering EEE,Chennai,Tamil Nadu,India,127

7. GRIET,Hyderabad,Telangana,India

Publisher

IEEE

Link

Reference18 articles.

1. Use of Texture Classification To Quantify Stem Cell Differentiation During Time Lapse Imaging;Pai;CS229 final project,2013

2. Good Cell, Bad Cell: Classification of Segmented Images for Suitable Quantification and Analysis;Macklin;CS229 final project,2012