Explaining decisions of deep neural networks used for fish age prediction.

ABSTRACT: Age-reading of fish otoliths (ear stones) is important for the sustainable management of fish resources. However, the procedure is challenging and requires experienced readers to carefully examine annual growth zones. In a recent study, convolutional neural networks (CNNs) have been demonstrated to perform reasonably well on automatically predicting fish age from otolith images. In the present study, we carefully investigate the prediction rule learned by such neural networks to provide insight into the features that identify certain fish age ranges. For this purpose, a recent technique for visualizing and analyzing the predictions of the neural networks was applied to different versions of the otolith images. The results indicate that supplementary knowledge about the internal structure i

SUBMITTER: Ordonez A 

PROVIDER: S-EPMC7304622 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature