Application of transfer-learning-based convolutional neural network in fossil image classification
Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials
Abstract:
Taxonomic identification of fossil is fundamental in paleontology research. However, traditional taxonomic methods rely heavily on expert experience and are inherently time-consuming. The development of deep learning architectures has made image-based fossil identification increasingly feasible. In this study, we apply an integrated framework that combines convolutional neural networks (CNN) with transfer learning to develop well-designed and trained machine-learning models for paleontological research. A fossil image dataset comprising 3, 358 images in five invertebrate classes and subclasses is compiled to train a VGG16 classifier. This dataset presents challenges due to its limited dataset size, high-dimensional data features, and inter-class/subclass sample imbalances. To address these issues, data augmentation and a two-phase transfer learning strategy are employed in the training process. In the first phase, only the final fully connected layer is randomly initialized and trained, while other layers are frozen with pretrained ImageNet weights. In the second phase, the whole model is fine-tuned with a reduced learning rate. The optimized model achieves an accuracy of 97.33%, a precision of 97.26%, a recall of 97.07%, and an F1-score of 97.16% on the test set. An ablation study is also conducted to test the effectiveness of transfer learning. A VGG16 classifier trained with random initialization achieves an accuracy of 74.89% on the test set. These results demonstrate that the constrained scientific datasets can be effectively utilized through (1) transfer-learning-enhanced CNN architectures; (2) strategic data augmentation protocols and (3) a progressive fine-tuning process. Notably, the model successfully accommodates heterogeneous input conditions of the fossil images, including varying imaging equipment, taxonomic diversity, complex image backgrounds, and environmental noises. This highlights the potential of the proposed framework for developing high-precision classifiers capable of fine-grained taxonomic discrimination at genus or species level, while maintaining computational efficiency. The achieved performance benchmarks suggest promising applications for automated fossil identification systems in paleontological research.
