| Summary: | Fossil studies are crucial for understanding species evolution and uncovering the dynamic structure of the Earth. However, the examination and interpretation of fossil specimens is inherently complex and time-consuming, particularly when dealing with thin sections where microfossils and non-fossil structures often coexist. This study presents a comparative analysis of image classification models, specifically CNN, ResNet, VGG, DenseNet, and EfficientNet, aimed at enhancing the detection and classification of fossil specimens. We developed a custom Convolutional Neural Network (CNN) architecture tailored to the identification of benthic foraminifera using the Endless Forams dataset. The custom CNN achieved a training accuracy of 99% and a validation accuracy of 88%, indicating its robustness. ResNet-50 and VGG-16 models achieved average accuracy scores of 90% and 86%, respectively, demonstrating their comparative effectiveness. Furthermore, ResNet-50 and VGG-16 models were identified as particularly effective due to their advanced capabilities in handling high-dimensional data and capturing detailed image features. Our findings provide a comprehensive understanding of each model’s performance, supported by rigorous statistical evaluation, offering insights into their strengths and limitations within the domain of fossil image classification.
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