Deep Learning Image Analysis of High-Throughput Toxicology Assay Images.

ABSTRACT: High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-consuming manual process, we have developed a method that uses deep learning to automatically classify digital assay images. We have trained a convolutional neural network (CNN) to perform binary and multi-class classification. The binary classifier binned assay images into healthy (comparable to untreated controls) and altered (not comparable to untreated-control) classes with >98% accuracy; the multi-class classifier assigned "Healthy," "Intermediate" and "Altered" labels to assay images with >95% accuracy. Our dataset comprised high-resolution

SUBMITTER: Tandon A 

PROVIDER: S-EPMC8955414 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature