Nanni, L.; Faldani, G.; Brahnam, S.; Bravin, R.; Feltrin, E. Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning. Preprints2023, 2023020396. https://doi.org/10.20944/preprints202302.0396.v1
APA Style
Nanni, L., Faldani, G., Brahnam, S., Bravin, R., & Feltrin, E. (2023). Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning. Preprints. https://doi.org/10.20944/preprints202302.0396.v1
Chicago/Turabian Style
Nanni, L., Riccardo Bravin and Elia Feltrin. 2023 "Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning" Preprints. https://doi.org/10.20944/preprints202302.0396.v1
Abstract
This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically Convolutional Neural Networks (CNNs), to analyze digital images of foraminifera taken at different illumination angles. The dataset is composed of 1437 groups of sixteen grayscale images, one group for each foraminifer, that are then converted to RGB images with various processing methods. These RGB images are fed into a set of CNNs, organized in an Ensemble Learning (EL) environment. The ensemble is built by training different networks using different approaches for creating the RGB images. The study finds that an ensemble of CNN models trained on different RGB images improves the system's performance compared to other state-of-the-art approaches. The proposed system was also found to outperform human experts in classification accuracy.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
