ArticleIntegrative organismal biology (Oxford, England)2026
Integrating Traditional Machine Learning and Convolutional Neural Networks Into the Geometric Morphometric Pipeline for Taxonomic Classification (GM-ML-CNN).
Article in Integrative organismal biology (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Geometric morphometrics (GM) is a well-established and cost-effective approach for quantifying shape variation and has been widely applied over the past few decades to resolve morphological differentiation and to discriminate among closely related taxa. A key analytical advantage of GM lies in its structured workflow, which transforms complex morphological features into multivariate shape data while inherently managing dimensionality. Consequently, GM outputs serve as powerful, optimized feature sets for training machine learning classifiers in taxonomic and ecological studies. We present an integrative framework combining GM, traditional machine learning (ML) algorithms, and convolutional neural networks (CNNs) for species discrimination. This study focused on the classification of digital wing images of
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