23. Robin J. Smith (2000) Morphology and ontogeny of Cretaceous ostracods with preserved appendages from Brazil. Palaeontology (43) part I, 63-89 https://onlinelibrary.wiley.com/doi/pdf/10.1111/1475-4983.00119
24 Priya Rani · Shallu Kotwal · Jatinder Manhas· Vinod Sharma · Sparsh Sharma (2022) Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments. Archives of Computational Methods in Engineering (2022) 29:1801–1837 [20231214e]
25 Qian Ge · Turner Richmond · Boxuan Zhong · Thomas M. Marchitto · Edgar J. Lobaton (2021) Enhancing the morphological segmentation of microscopic fossils through Localized Topology-Aware Edge Detection. Autonomous Robots (2021) 45:709–723 [20231214d]
26 Xiaokang Liu, Shouyi Jiang, Rui Wu, Wenchao Shu, Jie Hou, Yongfang Sun, Jiarui Sun, Daoliang Chu, Yuyang Wu, and Haijun Song (2023) Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks. Paleobiology, 49(1), 2023, pp. 1–22 [20231214c]
27 Chengbin Hou., Xinyu Lin., Hanhui Huang., Sheng Xu.,Junxuan Fan., Yukun Shi., Hairong Lv (2023) Fossil image identification using deep learning ensembles of data augmented multiviews. Methods Ecol Evol. 2023;14:3020–3034. [20231214b]
28 Tünde Cséfán , Em˝ oke Tóth (2018) Mid-Cretaceous/Albian (Cretaceous) ostracod assemblages from NW Hungary reflecting deep marine, nearshore and non-marine environments. Annales de Paléontologie 104 (2018) 267–289 [20231213b]
References for Ostracods image analysis (page 2)