Towards investigation of transfer learning framework for Globotruncanita genus and Globotruncana genus microfossils in Genus-Level and Species-Level prediction

Ozer I., Kocak I., Cetin O., Can Karaca A. C., KAYA ÖZER C., Gorur K.

Engineering Science and Technology, an International Journal, vol.48, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 48
  • Publication Date: 2023
  • Doi Number: 10.1016/j.jestch.2023.101589
  • Journal Name: Engineering Science and Technology, an International Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC, Directory of Open Access Journals
  • Keywords: Deep Learning, Globotruncana genus, Globotruncanita genus, Microfossil, Transfer Learning
  • Yıldız Technical University Affiliated: Yes


The applicability of digital imaging techniques and machine learning models to paleontological datasets is exploring the possibility of predicting microfossils extracted from the rock samples instead of the traditional identifying methodologies under the microscope in a one-by-one way via a domain expert. However, these processes, including labeling, are carried out manually and take a high time-consuming, especially for many quantities and diversity of complex morphological microfossil specimens. In this work, we propose a transfer learning framework based on a custom model CNN (Convolutional Neural Network) and diverse pre-trained deep models (ResNet50, Xception, InceptionV3, VGG6, MobileNet) trained with the millions of images for Globotruncanita genus and Globotruncana genus in genus-level and species-level prediction. The second primary advantage of our framework is able to provide better and more robust decisions for a limited number of microfossil images captured by the low-cost light microscope imaging technology. The comparison of the diverse methods was evaluated with different performance metrics, and the observation of the framework was made to perform high prediction scores reaching up to the outcomes (>99 % accuracy and > 0.99 AUC score for genus-level/>81 % accuracy and > 0.89 AUC score for species-level). As far as we know, this research study is the first attempt to investigate a transfer learning framework to predict the Globotruncanita genus and Globotruncana genus families at the genus-level and species-level microfossils. Overall, it may extend the existing literature on paleontological science and automated/quick classification manner.