Towards detection and classification of microscopic foraminifera using transfer learning

• Main Authors: Johansen, Thomas Haugland, Sørensen, Steffen Aagaard
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2020
• Subjects: Machine Learning cs.LG; Computer Vision and Pattern Recognition cs.CV; Machine Learning stat.ML; FOS Computer and information sciences; Barents Sea
• Online Access: https://dx.doi.org/10.48550/arxiv.2001.04782; https://arxiv.org/abs/2001.04782

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. : 6 pages, 5 figures. To be published in proceedings of Northern Lights Deep Learning Workshop 2020