Automated recognition of Meso-Cenozoic foraminifera from Senegalese sedimentary deposits using convolutional neural network

This study investigates the application of Convolutional Neural Networks (CNNs) for the automated classification of Meso-Cenozoic foraminifera from the West African margin, with a focus on planktonic, benthic, and agglutinated taxa. Three training datasets comprising 11,378 images were used to train CNN models for microfossil detection, genus-level, and species-level identification. Genus-level classification (72.4%) outperformed species-level identification (50.2%), as it reduced misclassifications from morphological variability and post-mortem degradation in closely related species. The genus-level CNN performed well for genera with distinct morphological features, such as Muricohedbergella sp. and Heterohelix sp., but struggled with Subbotina spp. and Dicarinella spp. due to shared morphological traits and diagenetic alterations. For benthic foraminifera, Nummulites sp. was easily identified, while Gavelinella sp. posed significant challenges. These results highlight the potential of CNNs for high-throughput classification, while also revealing limitations particularly for taxa with high morphological variability or diagenetic changes. The findings have important implications for biostratigraphy, suggesting that CNNs can enhance genus-level classification in biostratigraphic applications. Further advancements such as multi-view imaging and expanded training datasets could enhance the CNN performance. The integration of CNNs with traditional biostratigraphic methods could enhance the temporal resolution of foraminiferal biostratigraphy, further advancing both the classification process and the understanding of paleoenvironmental conditions.