Mentor
Alférez Salinas, Germán Harvey
Document Type
Thesis
Publication Date
Spring 4-28-2023
Abstract
Microfossil dinosaur teeth are studied by paleontologists in order to better under- stand dinosaurs. Currently, tooth classification is a long, manual, error-ridden process. Deep learning offers a solution that allows for an automated way of classifying images of these microfossil teeth. In this thesis, we aimed to use deep learning in order to develop an automated approach for classifying images of Pectinodon bakkeri teeth. The proposed model was trained using a custom topology and it classified the images based on clusters created via K-Means. The model had an accuracy of 71%, a precision of 71%, a recall of 70.5%, and an F1-score of 70.5%.
Recommended Citation
Bahn, Jacob A., "Automated Classification of Pectinodon Bakkeri Teeth Images Using Machine Learning" (2023). MS in Computer Science Project Reports. 9.
https://knowledge.e.southern.edu/mscs_reports/9
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Paleontology Commons