TY  - JOUR
AU  - Iban Berganzo-Besga
AU  - Hector A. Orengo
AU  - Felipe Lumbreras
AU  - Paloma Aliende
AU  - Monica N. Ramsey
PY  - 2022//
TI  - Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms
T2  - JArchSci
JO  - Journal of Archaeological Science
SP  - 105654
VL  - 148
N2  - This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability.
UR  - http://dx.doi.org/10.1016/j.jas.2022.105654
N1  - MSIAU; MACO; 600.167
ID  - Iban Berganzo-Besga2022
ER  -