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Foraminifera are single-celled marine organisms, which may have a planktic orbenthic lifestyle. During their life cycle they construct shells consisting ofone 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 countingmicrofossils is performed manually using a microscope and is very timeconsuming. Developing methods to automate this process is therefore consideredimportant across a range of research fields. The first steps towards developinga deep learning model that can detect and classify microscopic foraminifera areproposed. The proposed model is based on a VGG16 model that has been pretrainedon the ImageNet dataset, and adapted to the foraminifera task using transferlearning. Additionally, a novel image dataset consisting of microscopicforaminifera and sediments from the Barents Sea region is introduced.

Towards detection and classification of microscopic foraminifera using transfer learning