Towards detection and classification of microscopic foraminifera using transfer learning

• Published in: Proceedings of the Northern Lights Deep Learning Workshop
• Main Authors: Johansen, Thomas Haugland, Sørensen, Steffen Aagaard
• Format: Article in Journal/Newspaper
• Language: English
• Published: Septentrio Academic Publishing 2020
• Subjects: VDP::Mathematics and natural science: 400::Geosciences: 450::Marine geology: 466; VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Marin geologi: 466; Barents Sea
• Online Access: https://hdl.handle.net/10037/20559; https://doi.org/10.7557/18.5144

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.