Towards detection and classification of microscopic foraminifera using transfer learning
This work addresses the automation of microfossil classification for oceanography and climatology research, but it is incremental as it applies an existing method to a new dataset.
The authors tackled the time-consuming manual identification and counting of microscopic foraminifera fossils by proposing a deep learning model based on VGG16 with transfer learning, and introduced a new image dataset from the Barents Sea region.
Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology. Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields. The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.