Automatic identification of conodont species using fine-grained convolutional neural networks

Abstract

Conodonts are jawless vertebrates deposited in marine strata from the Cambrian to the Triassic that play an important role in geoscience research. The accurate identification of conodonts requires experienced professional researchers. The process is time-consuming and laborious and can be subjective and affected by the professional level and opinions of the appraisers. The problem is exacerbated by the limited number of experts who are qualified to identify conodonts. Therefore, a rapid and simple artificial intelligence method is needed to assist with the identification of conodont species. Although the use of deep convolutional neural networks (CNN) for fossil identification has been widely studied, the data used are usually from different families, genera or even higher-level taxonomic units. However, in practical geoscience research, geologists are often more interested in classifying species belonging to the same genus. In this study, we use five fine-grained CNN models on a dataset consisting of nine species of the conodont genus Hindeodus. Based on the cross-validation results, we show that using the Bilinear-ResNet18 model and transfer learning generates the optimal classifier. Area Under Curve (AUC) value of 0.9 on the test dataset was obtained by the optimal classifier, indicating that the performance of our classifier is satisfactory. In addition, although our study is based on a very limited taxa of conodonts, our research principles and processes can be used as a reference for the automatic identification of other fossils.