Morphological classification of hematophagous Diptera with Convolutional Neural Networks: A mapping of literature

Benjamín Paulino  Mendoza Contreras; Emmanuel Morales Garcia; Cecilia  Cruz López; Luis Enrique Gomez Medina

doi:10.33333/lajc.vol13n2.03

Authors

Benjamín Paulino Mendoza Contreras Veracruzana University https://orcid.org/0009-0000-3491-6234
Emmanuel Morales Garcia Veracruzana University https://orcid.org/0000-0002-6837-9227
Cecilia Cruz López Veracruzana University https://orcid.org/0000-0002-9156-5669
Luis Enrique Gomez Medina Veracruzana University https://orcid.org/0009-0009-1324-389X

DOI:

https://doi.org/10.33333/lajc.vol13n2.03

Keywords:

Entomology, Organism Classification, Deep Learning, Species, Identification, Morphology

Abstract

This review analyzes studies that primarily address the morphological classification of hematophagous Diptera, with limited mention of other insects. These networks have become increasingly important in morphological analysis through the accurate and efficient automatic identification of species, surpassing even traditional methods based on human observation. The main architectures used, such as VGG-16, YOLOv5, Faster R-CNN, Mask R-CNN, ResNet, and Swin Transformer-L are reviewed, highlighting their applications in the detection and identification of different anatomical parts. Common limitations are also mentioned, such as the need for large volumes of classified data and variability in image quality. Finally, current trends have been identified that point to the development of more robust hybrid models capable of recognizing new species and improving accuracy under real-world conditions. This literature mapping provides greater certainty and evidence regarding the most important identification methods in the field of entomology. These findings highlight the gap in literature related to the availability of public data, parameters used, data volume, image quality, and model evaluation, providing a solid foundation to guide future research in the field of entomology.

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Author Biographies

Benjamín Paulino Mendoza Contreras , Veracruzana University

My name is Benjamín Paulino Mendoza Contreras, and I am a student pursuing a Bachelor's Degree in Statistics at the Faculty of Statistics and Computer Science at the University of Veracruz, Xalapa region. I am currently 20 years old and am in the process of professional training in the field of statistics, developing skills in data analysis, the application of statistical methods, and the use of computer tools for research and decision-making. My academic interest focuses on the development and application of quantitative techniques for problem solving in various fields, contributing to the advancement of statistical knowledge and practice.
Emmanuel Morales Garcia, Veracruzana University

He holds a Bachelor's degree in Statistical Sciences and Techniques and a Specialist in Statistical Methods from the University of Veracruz, with a Master's degree in Geospatial Information Sciences from the Geo Center, Mexico City (CONAHCYT Center). He is currently pursuing a PhD in Computer Science at the University of Veracruz. He is a professor in the Bachelor's program in Statistics and in the Specialization in Statistical Methods at the same university, where he has supervised 12 undergraduate theses and 4 specialized theses. He also has experience as a Statistical Analyst at the Government Program Office of the State of Veracruz. My research interests include computational methodologies, statistical programming, multivariate statistics, spatial analysis, data science, and statistical models, with applications in biology, medicine, and administrative and social sciences. He has participated in various national and international conferences.
Cecilia Cruz López , Veracruzana University

She holds a PhD in Educational Research (Universidad Veracruzana), a Master of Science degree specializing in Applied Statistics (ITESM Monterrey Campus), and a Bachelor of Statistics degree (Universidad Veracruzana). She has published four books, nine book chapters, ten research articles, and one article in Extended Proceedings. She has supervised three master's theses, 24 specialized theses, and 26 undergraduate theses. Her research interests include Statistics Education and Applications of Statistical Methodology. She collaborated with CENEVAL from 2010 to 2017, advising, evaluating, and developing items for the Extra-Es Statistics exam. She is currently the coordinator of the Specialization in Statistical Methods.

References

[1] Adhane, G., Dehshibi, M. M., & Masip, D. (2021). A deep convolutional neural network for classification of Aedes albopictus mosquitoes. IEEE Access, 9, 72681–72690.

[2] Elhamod, M., et al. (2022). Hierarchy-guided neural network for species classification. Methods in Ecology and Evolution, 13(3), 642–652.

[3] Zhou, Z., et al. (2023). EchoAI: A deep-learning based model for classification of echinoderms in global oceans. Frontiers in Marine Science, 10, 1147690.

[4] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

[5] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.

[6] Oliveira, M. B., et al. (2025). Classification of animal species via deep neural networks and species distribution modeling: A systematic review. Artificial Intelligence Review, 58, 230.

[7] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

[8] Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.

[9] Miao, Z., et al. (2019). Insights and approaches using deep learning to classify wildlife. Scientific Reports, 9, 8137.

[10] Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In Proceedings of the International Conference on Learning Representations (ICLR).

[11] Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (Vol. 28, pp. 91–99).

[12] Khanam, R., & Hussain, M. (2024). What is YOLOv5: A deep look into the internal features of the popular object detector. arXiv:2407.20892.

[13] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 779–788).

[14] Xie, S., et al. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5987–5995).

[15] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778).

[16] Lin, T. Y., et al. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 2980–2988).

[17] He, K., et al. (2017). Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 2961–2969).

[18] Liu, Z., et al. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 10012–10022).

[19] Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861.

[20] Minakshi, M., et al. (2020). A framework based on deep neural networks to extract anatomy of mosquitoes from images. arXiv:2007.14725.

[21] Sauer, F. G., et al. (2024). A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images. Scientific Reports, 14(1), 3094.

[22] Nolte, K., et al. (2025). Potentials and limitations in the application of convolutional neural networks for mosquito species identification using wing images. PLoS Computational Biology, 21(9), e1013435.

[23] Martinet, J.-P., et al. (2021). Wing morphometrics of Aedes mosquitoes from north-eastern France. Insects, 12(4), 341.

[24] Cannet, A., et al. (2023). Wing interferential patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest. Scientific Reports, 13(1), 11956.

[25] Venegas, P., et al. (2020). An approach to automatic classification of Culicoides species by learning the wing morphology. PLoS ONE, 15(11), e0241798.

[26] Gireesh, A., & Noortaj, S. (2025). Hybrid machine learning approach for mosquito species classification using wing beat analysis. International Journal of Scientific Research in Science, Engineering and Technology, 12(3), 1062–1070. https://ijsrset.com/index.php/home/article/view/IJSRSET2512124

[27] Zhao, D., et al. (2022). A Swin transformer-based model for mosquito species identification. Scientific Reports, 12(1), 18664.

[28] Lee, S., Kim, H., & Cho, B.-K. (2023). Deep learning-based image classification for major mosquito species inhabiting Korea. Insects, 14(6), 526.

[29] Goodwin, A., et al. (2021). Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection. Scientific Reports, 11(1), 24040.