Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells

• Published in: PloS one Vol. 16; no. 3; p. e0246988
• Main Authors: Ghafari, Mehran, Clark, Justin, Guo, Hao-Bo, Yu, Ruofan, Sun, Yu, Dang, Weiwei, Qin, Hong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.03.2021; Public Library of Science (PLoS)
• Subjects: Accuracy; Aging; Architecture; Biology and Life Sciences; Classification; Computer and Information Sciences; Computer programs; Computer science; Datasets; Deep learning; Drafting software; Editing; Electronic mail; Engineering; Engineering and Technology; Environmental science; Genetic aspects; Geology; Handwriting; Identification and classification; Image classification; Image resolution; Machine learning; Medicine; Methods; Microfluidics; Neural networks; Physiological aspects; Research and Analysis Methods; Reviews; Software; Training; Yeast; Yeast fungi; Yeasts; United States; United States > US; Texas; Tennessee

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures t...