Published February 12, 2026 | Version v1
Dataset Restricted

AegPupaCNN: A Custom Convolutional Neural Network and Comparative Models for Sex Classification of Aedes aegypti Pupae Using Individual Images

Authors/Creators

  • 1. Philippine Nuclear Research Institute
  • 2. ROR icon De La Salle University

Description

Data Description

  • Dataset Title: Data from: AegPupaCNN: A Custom Convolutional Neural Network and Comparative Models for Sex Classification of Aedes aegypti Pupae Using Individual Images
  • Description of Experimental Efforts: This dataset was generated to evaluate the efficacy of Convolutional Neural Networks (CNNs) for automating the sex classification of Aedes aegypti pupae, a process essential for the Sterile Insect Technique (SIT). Experimental data collection involved rearing Ae. aegypti mosquitoes in a controlled laboratory setting across six generations. Individual pupae were imaged using a digital microscope (SVBONY M-201) from both lateral and ventral views to capture sexually dimorphic morphological features. The dataset provided here contains the test performance metrics and raw prediction outputs for five CNN architectures (AegPupaCNN, ResNet50, MobileNet, VGG16, and InceptionV3), which were trained on five generations of training data and cross-validated/ tested on an independent sixth generation.
  • Geographic Location: Quezon City, Philippines.
  • Funding: Department of Science and Technology – Science Education Institute (DOST-SEI), Philippines.

 

Files and variables

File: AegPupaCNN_lateral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the custom AegPupaCNN model evaluated on lateral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables:

  • Sheet 1 (Raw Data):
    • Filename: Unique identifier string for the specific image file tested (e.g., "female\F1...").
    • True Label: The actual biological sex of the pupa (Ground truth). Values: "female", "male".
    • Predicted Label: The classification output generated by the model. Values: "female", "male".
  • Sheet 2 (Metrics):
    • Metric: Name of the statistical performance measure. Values: "Accuracy", "Precision", "Recall", "F1-Score", "ROC-AUC".
    • Value: The calculated numerical score for the corresponding metric (Range: 0.0–1.0 or 0–100%).

File: InceptionV3_lateral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the InceptionV3 model evaluated on lateral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables

  • Same variables as AegPupaCNN_lateral.xlsx.

File: MobileNet_lateral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the MobileNet model evaluated on lateral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables

  • Same variables as AegPupaCNN_lateral.xlsx.

File: ResNet50_lateral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the ResNet50 model evaluated on lateral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables

  • Same variables as AegPupaCNN_lateral.xlsx.

File: VGG16_lateral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the VGG16 model evaluated on lateral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables

  • Same variables as AegPupaCNN_lateral.xlsx.

File: AegPupaCNN_ventral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the custom AegPupaCNN model evaluated on ventral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables

  • Same variables as AegPupaCNN_lateral.xlsx.

File: InceptionV3_ventral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the InceptionV3 model evaluated on ventral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables

  • Same variables as AegPupaCNN_lateral.xlsx.

File: MobileNet_ventral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the MobileNet model evaluated on ventral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables

  • Same variables as AegPupaCNN_lateral.xlsx.

File: ResNet50_ventral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the ResNet50 model evaluated on ventral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables

  • Same variables as AegPupaCNN_lateral.xlsx.

File: VGG16_ventral.xlsx

Description: Contains raw prediction data and calculated performance metrics for the VGG16 model evaluated on ventral view images of Aedes aegypti pupae. Sheet 1 provides instance-level predictions for the independent test set. Sheet 2 provides summary performance metrics.

Variables

  • Same variables as AegPupaCNN_lateral.xlsx.

 

Code/software

  • Software needed to view the data: The data files are in standard Microsoft Excel (.xlsx) format. They can be viewed and edited using any spreadsheet software.
  • Software used to generate the data: The data was generated using custom scripts written in Python 3.12. The following key libraries were used for image preprocessing, model training, and performance evaluation:
    • TensorFlow (Keras API): For building and training the CNN architectures.
    • Scikit-learn: For calculating performance metrics (Precision, Recall, F1-Score, ROC-AUC) and generating classification reports.
    • OpenCV (cv2): For image preprocessing tasks including background removal and resizing.
    • Pandas: For structuring the output data and exporting to .xlsx format.

 

Access information

Other publicly accessible locations of the data:

  • None.

Data was derived from the following sources:

  • The data provided in this repository are original and were generated by the authors during the study. They were not derived from external public datasets. The biological material (Aedes aegypti mosquitoes) used to generate the image data was obtained from the mosquito-rearing facility at the Philippine Nuclear Research Institute.

Files

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Additional details

Dates

Collected
2024-08
Data from: AegPupaCNN: A Custom Convolutional Neural Network and Comparative Models for Sex Classification of Aedes aegypti Pupae Using Individual Images
Collected
2025-01
Data from: AegPupaCNN: A Custom Convolutional Neural Network and Comparative Models for Sex Classification of Aedes aegypti Pupae Using Individual Images

Software

Development Status
Active

References

  • Abadi, M., P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng. 2016. "TensorFlow: A System for Large-Scale Machine Learning." Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI '16): 265–283.
  • Adhane, G., M. M. Dehshibi, and D. Masip. 2021. "A Deep Convolutional Neural Network for Classification of Aedes albopictus Mosquitoes." IEEE Access 9: 72681–72690. https://doi.org/10.1109/access.2021.3079700.
  • Akter, M., M. S. Hossain, T. U. Ahmed, and K. Andersson. 2021. "Mosquito Classification Using Convolutional Neural Network with Data Augmentation." In Advances in Intelligent Systems and Computing: 865–879. Springer International Publishing. https://doi.org/10.1007/978-3-030-68154-8_74.
  • Asmai, S. A., M. N. D. M. Zukhairin, A. S. M. Jaya, A. F. N. A. Rahman, and Z. B. A. Abas. 2019. "Mosquito Larvae Detection Using Deep Learning." International Journal of Innovative Technology and Exploring Engineering 8, no. 12: 804–809. https://doi.org/10.35940/ijitee.L3213.1081219
  • Carvalho, D. O., D. Nimmo, N. Naish, A. R. McKemey, P. Gray, A. B. B. Wilke, M. T. Marrelli, J. F. Virginio, L. Alphey, and M. L. Capurro. 2014. "Mass Production of Genetically Modified Aedes aegypti for Field Releases in Brazil." Journal of Visualized Experiments 83: e3579. https://doi.org/10.3791/3579.
  • Christophers, S. R. 1960. Aedes aegypti (L.) the Yellow Fever Mosquito: Its Life History, Bionomics and Structure. Cambridge University Press.
  • Clements, A. N. 1992. The Biology of Mosquitoes. Volume 1: Development, Nutrition and Reproduction. London: Chapman & Hall.
  • De Araújo, T. O., V. L. de Miranda, and R. Gurgel-Gonçalves. 2024. "AI-Driven Convolutional Neural Networks for Accurate Identification of Yellow Fever Vectors." Parasites & Vectors 17: 329. https://doi.org/10.1186/s13071-024-06406-2.
  • Dyck, V. A., J. Hendrichs, and A. S. Robinson. 2021. Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management. Boca Raton, FL: CRC Press. https://doi.org/10.1201/9781003035572.
  • Focks, D. A. 1980. "An Improved Separator for the Developmental Stages, Sexes, and Species of Mosquitoes (Diptera: Culicidae)." Journal of Medical Entomology 17: 567–568. https://doi.org/10.1093/jmedent/17.6.567.
  • Gong, J. T., W. Mamai, X. Wang, J. Zhu, Y. Li, J. Liu, Q. Tang, Y. Huang, J. Zhang, J. Zhou, H. Maiga, N. S. B. Somda, C. Martina, S. S. Kotla, T. Wallner, J. Bouyer, and Z. Xi. 2024. "Upscaling the Production of Sterile Male Mosquitoes with an Automated Pupa Sex Sorter." Science Robotics 9: eadj6261. https://doi.org/10.1126/scirobotics.adj6261.
  • Goodwin, A., S. Padmanabhan, S. Hira, M. Glancey, M. Slinowsky, R. Immidisetti, and others. 2021. "Mosquito Species Identification Using Convolutional Neural Networks with a Multitiered Ensemble Model for Novel Species Detection." Scientific Reports 11, no. 1: 13656. https://doi.org/10.1038/s41598-021-92891-9.
  • Gunathilaka, N., T. Ranathunge, L. Udayanga, A. Wijegunawardena, J. R. L. Gilles, and W. Abeyewickreme. 2019. "Use of Mechanical and Behavioural Methods to Eliminate Female Aedes aegypti and Aedes albopictus for Sterile Insect Technique and Incompatible Insect Technique Applications." Parasites & Vectors 12: 148. https://doi.org/10.1186/s13071-019-3398-7
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. "Deep Residual Learning for Image Recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 770–778. https://doi.org/10.1109/CVPR.2016.90.
  • Hila, A. M. J., and G. B. Obra. 2023. "Evaluation of Blood-Feeding Methods for Rearing Dengue Mosquito, Aedes aegypti L. (Diptera: Culicidae)." Philippine Journal of Science 152, no. 3: 903–912.
  • Howard, A. G., M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, and others. 2017. "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications." arXiv preprint arXiv:1704.04861.
  • Javed, N., A. J. López-Denman, P. N. Paradkar, and A. Bhatti. 2023. "EggCountAI: A Convolutional Neural Network-Based Software for Counting of Aedes aegypti Mosquito Eggs." Parasites & Vectors 16: 341. https://doi.org/10.1186/s13071-023-05956-1.
  • Javed, N., A. J. López-Denman, P. N. Paradkar, and A. Bhatti. 2024a. "LarvaeCountAI: A Robust Convolutional Neural Network-Based Tool for Accurately Counting the Larvae of Culex annulirostris Mosquitoes." Acta Tropica 260: 107468. https://doi.org/10.1016/j.actatropica.2024.107468.
  • Javed, N., A. J. López-Denman, P. N. Paradkar, and A. Bhatti. 2024b. "FlightTrackAI: A Robust Convolutional Neural Network-Based Tool for Tracking the Flight Behaviour of Aedes aegypti Mosquitoes." Royal Society Open Science 11, no. 10: 240923. https://doi.org/10.1098/rsos.240923.
  • Joelianto, E., M. I. Mandasari, D. B. Marpaung, N. D. Hafizhan, T. Heryono, M. E. Prasetyo, Dani, S. Tjahjani, T. Anggraeni, and I. Ahmad. 2024. "Convolutional Neural Network-Based Real-Time Mosquito Genus Identification Using Wingbeat Frequency: A Binary and Multiclass Classification Approach." Ecological Informatics 80: 102495. https://doi.org/10.1016/j.ecoinf.2024.102495.
  • Kim, J., J. Oh, and T. Y. Heo. 2021. "Acoustic Classification of Mosquitoes Using Convolutional Neural Networks Combined with Activity Circadian Rhythm Information." International Journal of Interactive Multimedia and Artificial Intelligence 7, no. 2: 59–65. https://doi.org/10.9781/ijimai.2021.08.009.
  • Kingma, D. P., and J. Ba. 2014. "Adam: A Method for Stochastic Optimization." arXiv preprint arXiv:1412.6980.
  • Kittichai, V., T. Pengsakul, K. Chumchuen, Y. Samung, P. Sriwichai, N. Phatthamolrat, T. Tongloy, K. Jaksukam, S. Chuwongin, and S. Boonsang. 2021. "Deep Learning Approaches for Challenging Species and Gender Identification of Mosquito Vectors." Scientific Reports 11: 4838. https://doi.org/10.1038/s41598-021-84219-4.
  • LeCun, Y., Y. Bengio, and G. Hinton. 2015. "Deep Learning." Nature 521, no. 7553: 436–444. https://doi.org/10.1038/nature14539.
  • Lee, S., H. Kim, and B.-K. Cho. 2023. "Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea." Insects 14, no. 6: 526. https://doi.org/10.3390/insects14060526.
  • Mamai, W., O. Bueno-Masso, T. Wallner, S. A. Nikièma, S. Meletiou, D. Liang, F. Balestrino, H. Yamada, and J. Bouyer. 2024. "Efficiency Assessment of a Novel Automatic Mosquito Pupae Sex Separation System in Support of Area-Wide Male-Based Release Strategies." Scientific Reports 14: 9170. https://doi.org/10.1038/s41598-024-58067-x.
  • Mulchandani, P., M. U. Siddiqui, and P. Kanani. 2019. "Real-Time Mosquito Species Identification Using Deep Learning Techniques." International Journal of Engineering and Advanced Technology 9, no. 2: 2000–2003. https://doi.org/10.35940/ijeat.B2929.129219.
  • Njaime, F. C. B. F. P., R. C. Máspero, A. de S. Leandro, and R. Maciel-de-Freitas. 2024. "Automated Classification of Mixed Populations of Aedes aegypti and Culex quinquefasciatus Mosquitoes Under Field Conditions." Parasites & Vectors 17, no. 1. https://doi.org/10.1186/s13071-024-06417-z.
  • Nolte, K., F. G. Sauer, J. Baumbach, P. Kollmannsberger, C. Lins, and R. Lühken. 2024. "Robust Mosquito Species Identification From Diverse Body and Wing Images Using Deep Learning." Parasites & Vectors 17, no. 1. https://doi.org/10.1186/s13071-024-06459-3.
  • Obra, G. B., and A. M. V. Javier-Hila. 2021. "Colonization and Various Parameters Affecting Egg Hatch and Development of the Old Balara Strain (Philippines), Aedes aegypti." Philippine Journal of Science 150, no. 3: 833–841.
  • Obra, G. B., A. M. J. Hila, A. O. Dimaano, and S. S. Resilva. 2022. "Evaluation of Larval Diets for Mass Rearing of Aedes aegypti L. (Diptera: Culicidae)." Mindanao Journal of Science and Technology 20 (Special Issue 1): 114–132.
  • Okayasu, K., K. Yoshida, M. Fuchida, and A. Nakamura. 2019. "Vision-Based Classification of Mosquito Species: Comparison of Conventional and Deep Learning Methods." Applied Sciences 9, no. 18: 3935. https://doi.org/10.3390/app9183935
  • Paixao, E. S., M. G. Teixeira, and L. C. Rodrigues. 2017. "Zika, Chikungunya and Dengue: The Causes and Threats of New and Re-Emerging Arboviral Diseases." BMJ Global Health 3 (Suppl 1): e000530. https://doi.org/10.1136/bmjgh-2017-000530.
  • Sauer, F. G., M. Werny, K. Nolte, C. Villacañas de Castro, N. Becker, E. Kiel, and R. Lühken. 2024. "A Convolutional Neural Network to Identify Mosquito Species (Diptera: Culicidae) of the Genus Aedes by Wing Images." Scientific Reports 14, no. 1. https://doi.org/10.1038/s41598-024-53631-x.
  • Selvaraju, R. R., M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. 2017. "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization." 2017 IEEE International Conference on Computer Vision (ICCV): 618–626. https://doi.org/10.1109/ICCV.2017.74.
  • Siddiqua, R., S. Rahman, and J. Uddin. 2021. "A Deep Learning-Based Dengue Mosquito Detection Method Using Faster R-CNN and Image Processing Techniques." Annals of Emerging Technologies in Computing 5, no. 3: 11–23. https://doi.org/10.33166/aetic.2021.03.002.
  • Siddiqui, A. A., and C. Kayte. 2023. "Transfer Learning for Mosquito Classification Using VGG16." In Advances in Intelligent Systems Research: 471–484. Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-196-8_36.
  • Simonyan, K., and A. Zisserman. 2014. "Very Deep Convolutional Networks for Large-Scale Image Recognition." arXiv preprint arXiv:1409.1556.
  • Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. 2016. "Rethinking the Inception Architecture for Computer Vision." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 2818–2826. https://doi.org/10.1109/CVPR.2016.308.
  • Vamsi, B., A. Al Bataineh, and B. P. Doppala. 2024. "Machine Learning-Based Classification of Mosquito Wing Beats Using Mel Spectrogram Images and Ensemble Modeling." Traitement Du Signal 41, no. 4: 2093–2101. https://doi.org/10.18280/ts.410437.
  • Weng, S.-C., I. Antoshechkin, E. Marois, and O. S. Akbari. 2023. "Efficient Sex Separation by Exploiting Differential Alternative Splicing of a Dominant Marker in Aedes aegypti." PLoS Genetics 19, no. 11: e1011065. https://doi.org/10.1371/journal.pgen.1011065.
  • Yamany, A. S., F. K. Adham, and R. Abdel-Gaber. 2024. "Morphological Description of the Pupa of Aedes aegypti (Diptera: Culicidae) Using a Scanning Electron Microscope." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 76, no. 1: 43–54. https://doi.org/10.1590/1678-4162-13120.