Fossil brachiopod identification using a new deep convolutional neural network

Wang, Haizhou; Li, Chufan; Zhang, Zhifei; Kershaw, Stephen; Holmer, Lars E.; Zhang, Yang; Wei, Keyi; Liu, Peng

doi:10.1016/j.gr.2021.09.011

Cited by 14 publications

(9 citation statements)

References 18 publications

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“…Although we independently built a dataset containing >16,000 images, it is still small for machine learning. Most studies in automatic fossil identification have focused on a few categories and large sample sizes ( Liu & Song, 2020 ; Liu et al, 2023 ; Niu & Xu, 2022 ; Wang et al, 2022 ), which undoubtedly helps improve performance. Niu & Xu (2022) used a dataset of 34,000 graptolites to perform an automatic identification study of 41 genera, which resulted in 86% accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Convolutional neural networks (CNNs) perform well in general recognition and have been used in the automatic identification of palaeontological fossils ( Dionisio et al, 2020 ; Liu & Song, 2020 ; Kiel, 2021 ; Liu et al, 2023 ; Niu & Xu, 2022 ; Wang et al, 2022 ; Ho et al, 2023 ). In this study, three pre-trained models of convolutional neural networks with good classification performance on the ImageNet dataset ( Deng et al, 2009 ) namely VGG-16 ( Simonyan & Zisserman, 2014 ), Inception-ResNet-v2 ( Szegedy et al, 2017 ), and EfficientNetV2s ( Tan & Le, 2021 ) were selected and suitably modified ( Fig.…”

Section: Methodsmentioning

confidence: 99%

“…To reduce the workload and work difficulty for researchers, automatic fossil identification methods relying on machine learning have been proposed extensively in recent years, among which models using convolutional neural networks (CNNs) ( e.g ., VGG-16 ( Simonyan & Zisserman, 2014 ), Inception-ResNet ( Szegedy et al, 2017 ), GoogLeNet ( Szegedy et al, 2015 ), etc .) have achieved good results ( Dionisio et al, 2020 ; Liu & Song, 2020 ; Liu et al, 2023 ; Niu & Xu, 2022 ; Wang et al, 2022 ; Ho et al, 2023 ). Other supervised ( e.g.…”

Section: Introductionmentioning

confidence: 96%

“…Automatic identification of brachiopods has been carried out previously. Wang et al (2022) used the transposed convolutional neural network to realize the automatic identification of fossils with a relatively small dataset and they achieved 97% identification accuracy for five brachiopod species based on 630 training images. In this study, we enabled the automatic identification of hundreds of taxa (bivalve and brachiopod) based on a newly established fossil dataset.…”

Section: Introductionmentioning

confidence: 99%

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Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning

Sun,

Liu,

Huang

et al. 2023

PeerJ

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Fossil identification is an essential and fundamental task for conducting palaeontological research. Because the manual identification of fossils requires extensive experience and is time-consuming, automatic identification methods are proposed. However, these studies are limited to a few or dozens of species, which is hardly adequate for the needs of research. This study enabled the automatic identification of hundreds of species based on a newly established fossil dataset. An available “bivalve and brachiopod fossil image dataset” (BBFID, containing >16,000 “image-label” data pairs, taxonomic determination completed) was created. The bivalves and brachiopods contained in BBFID are closely related in morphology, ecology and evolution that have long attracted the interest of researchers. We achieved >80% identification accuracy at 22 genera and ∼64% accuracy at 343 species using EfficientNetV2s architecture. The intermediate output of the model was extracted and downscaled to obtain the morphological feature space of fossils using t-distributed stochastic neighbor embedding (t-SNE). We found a distinctive boundary between the morphological feature points of bivalves and brachiopods in fossil morphological feature distribution maps. This study provides a possible method for studying the morphological evolution of fossil clades using computer vision in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning

Sun,

Liu,

Huang

et al. 2023

PeerJ

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“…Wang et al. 28 developed a temporal convolutional neural network for a dataset of five distinct species, emphasizing the importance of high accuracy in brachiopod fossil identification. Pires et al.…”

Section: Main Textmentioning

confidence: 99%