Computer vision meets microbiology: deep learning algorithms for classifying cell treatments in microscopy images
Kuupäev
2023
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Tartu Ülikool
Abstrakt
Cell classification is one of the most complex challenges in cellular research that has
significant importance to personalised medicine, cancer diagnostics and disease prevention.
The accurate classification of cells based on their unique characteristics provides valuable
insights into a patient's health status and in guiding treatment decisions. Thanks to recent
technological advancements, cellular research has experienced significant progress in the use
of deep learning and has become a valuable tool for tackling complicated tasks such as cell
classification. In this study, we explored the capability of state-of-the-art deep learning
models such as ResNet, ViT and Swin Transformer to automatically classify brightfield and
fluorescent microscopy images across single and multiple channels into four cell treatments:
Palbociclib, MLN8237, AZD1152, and CYC116. The results have revealed that Swin
Transformer surpasses the other models for cell treatment classification on multi-channel
fluorescent and brightfield images, achieving the highest accuracy of 86% and 59%,
correspondingly. However, the highest accuracy achieved on single-channel brightfield
images was 61%, using the ResNet-50 model. The previous research has shown that
combining multiple channels yields better performance which necessitates further
investigation into the capacity of deep learning models for automating the cell treatment
classification of single- and multi-channel brightfield microscopy images.
Kirjeldus
Märksõnad
machine learning, deep learning, neural networks, image classification