A mobile hybrid deep learning approach for classifying 3D-like representations of Amazonian lizards

Arthur Gonsales Vaz¹Roger Pinho de Oliveira²Caio De Oliveira Bastos^1,3Elena Almeida de Carvalho²Bruno Duarte Gomes^3*

• ¹Departamento de Ciência de Dados, Instituto Tecnológico Vale, Belém, Brazil
• ²Centro de Ciências Biológicas e da Saúde, Universidade da Amazônia, Belém', Brazil
• ³Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil

Image classification is a highly significant field in machine learning (ML), especially when applied to address longstanding and challenging issues in the biological sciences, such as specie recognition and biodiversity conservation. In this study, we present the development of a hybrid machine learning-based tool suitable for deployment on mobile devices. This tool is aimed at processing and classifying three-dimensional samples of endemic lizard species from the Amazon rainforest. The dataset used in our experiment was collected at the Museu Paraense Emílio Goeldi (MPEG), Belém-PA, Brazil, and comprises three species: a) Anolis fuscoauratus; b) Hoplocercus spinosus; and c) Polychrus marmoratus. We compared the effectiveness of four artificial neural networks (ANN) for feature extraction: a) MobileNet; b) MobileNetV2; c) MobileNetV3-Small; and d) MobileNetV3-Large. Additionally, we evaluated five classical ML models for classifying the extracted patterns: a) Support Vector Machine (SVM); b) GaussianNB (GNB); c) AdaBoost (ADB); d) K-Nearest Neighbors (KNN); and e) Random Forest (RF). The performance metrics of all classifiers were very close, we used the McNemar's test on each model's confusion matrix to evaluate and compare their statistical significance. Our best model was a combination of a 2.9 million parameters MobileNetV3-Small as the feature extractor, with a linear kernel-based SVM as the classifier, which achieved accuracy of 0.955, precision of 0.948, recall of 0.948, and f1-score of 0.948. The results indicated that the use of a small deep learning (DL) model, in combination with a classical ML algorithm, emerges as a viable technique for classifying threedimensional representations of lizard species samples. Such an approach facilitates taxonomic identification work for professionals in the field and provides a tool adaptable for integration into mobile data recording equipment, such as smartphones, and benefiting from more morphological features extracted from threedimensional samples instead of two-dimensional images.