Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images.

ABSTRACT: Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estimates are inconsistent, as several readings by the same human expert might result in different ages assigned to the same otolith, in addition to an inherent bias between readers. To improve efficiency and resolve inconsistent results in the age reading from otolith images by human experts, an automated procedure based on convolutional neural networks (CNNs), a class of deep learning models suitable for image processing, is investigated. We applied CNNs that perform image regression to estimate the age of Greenland halibut (Reinhardtius hippoglos

SUBMITTER: Martinsen I 

PROVIDER: S-EPMC9635702 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature