Application of neural networks for chitinozoans recognition in images

Abstract

Paleontological identification of microfauna using automated image recognition represents an innovative practical application of existing software methods for analysis and classification based on computer vision and machine learning technologies. The development of software capable of recognition of Chitinozoans in images will simplify and speed up the processing of large amounts of microfossil data. The use of neural networks for image analysis is also possible for other groups of paleoorganics. Chitinozoans have a number of advantages that allow a step-by-step assessment of the applicability of automated image recognition technology for biostratigraphic problems compared to other groups of microfossils. The artificial paleontological classification of Chitinozoa is based on clear morphological characteristics and can be formalized. At the first stage of solving recognition problems, a classification function was constructed that predicts class, the fossil belongs to, based on the input feature vector — either "chitinozoa" or "non-chitinozoa". The developed model of the Chitinozoans recognition algorithm showed a high degree of accuracy (more than 98 %).