Multi-Label Benthic Foraminifera Identification with Convolutional Neural Networks

Geological studies are of great importance in order to observe the change of living species over the years, to make inferences by using the information provided by the observed species, and to understand the developing and changing structure of the world we live in over the years. However, the examination and interpretation of fossil specimens is a complex and lengthy process. Especially in thin sections where microfossil studies are carried out, more than one fossil and non-fossil structures are often observed together. The detection and classification of fossil specimens with the help of computers simplifies this process as much as possible compared to manual classification processes. This study represents a comparative analysis of three distinct image classification models: CNN, ResNet, and VGG. We developed a custom Convolutional Neural Network (CNN) architecture aimed at advancing the analysis and classification of geological specimens, particularly focusing on the Endless Forams dataset for the identification of benthic foraminiferas. This approach significantly improves the precision of fossil identification, leveraging deep learning to interpret complex image data efficiently. Additionally, we have identified the ResNet-50 and VGG-16 models as optimal for our research purposes due to their advanced capabilities in handling high-dimensional data and their effectiveness in capturing detailed image features. The findings, application for benthic foraminiferas reveal significant insights into the models' performance, underscored by rigorous statistical evaluation, offering a comprehensive understanding of their capabilities and limitations within the realm of image classification.