Abstract
Microfossils, tiny fossils whose study requires the use of a microscope, have been widely applied in many fields of earth, life, and environmental sciences. The abundance and high diversity of microfossils, as well as the need for rapid identification, call for automated methods to classify microfossils. In this study, we constructed an open dataset of three-dimensional (3D) microfossils and proposed a deep learning-based approach for microfossil classification. The dataset, named `Archives of Digital Morphology' (ADMorph), currently contains more than ten thousand 3D models from five classes of 410 million-year-old fishes. The deep learning-based method includes data preprocessing, feature extraction, and 3D microfossil model classification. To assess the method performance and dataset representability, we performed extensive experiments. Compared with multiview convolutional neural networks (MVCNN) (91.54%), PointNet (64.13%), and VoxNet (78.15%), the method proposed herein had higher accuracy (97.60%) on the experimental dataset. We also verified data preprocessing (92.36%) and feature extraction (97.10%). We combined them to obtain the macroaveraging accuracy of 97.60%, the highest accuracy of 100%, and the lowest accuracy of 88.78%. We suggest that the proposed method can be applied to other 3D fossils and biomorphological research fields. The fast-accumulating 3D fossil models might become a source of information-rich datasets for deep learning.
Highlights
Microfossils, which are generally less than 5 mm in size, contain a wealth of information from small rock samples
2) We proposed an automated method for 3D microfossil model classification based on deep neural networks (DNNs) and support vector machine (SVM)
Compared with VoxNet, PointNet, and multiview convolutional neural networks (MVCNN), the accuracy of our method was better by 19.45%, 33.47%, and 6.06%, respectively
Summary
Microfossils, which are generally less than 5 mm in size, contain a wealth of information from small rock samples. They are useful to the study of earth, life, and environmental sciences, such as biostratigraphy, paleoclimatology, paleoceanography, hydrocarbon exploration, and evolutionary biology [1]–[3]. Features, using microscopes or electron microscopes and making thin sections These traditional methods are time consuming and require considerable expertise due to the vast quantity and high diversity of microfossils. Paleontologists apply geometric morphometrics to identify and classify fossils [4]–[6]. These methods are not fully automated, and paleontologists need to define many landmarks and indices for the measurement.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
