Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification

Mila Yoselyn Pacompia Machaca, Milagros Lizet Mayta Rosas, Eveling Castro-Gutierrez, Henry Abraham Talavera Diaz, Victor Luis Vasquez Huerta

Research output: Contribution to journalArticlepeer-review


Gastrointestinal parasitic diseases represent a latent problem in developing countries; it is necessary to create a support tools for the medical diagnosis of these diseases, it is required to automate tasks such as the classification of samples of the causative parasites obtained through the microscope using methods like deep learning. However, these methods require large amounts of data. Currently, collecting these images represents a complex procedure, significant consumption of resources, and long periods. Therefore it is necessary to propose a computational solution to this problem. In this work, an approach for generating sets of synthetic images of 8 species of parasites is presented, using Deep Convolutional Adversarial Generative Networks (DCGAN). Also, looking for better results, image enhancement techniques were applied. These synthetic datasets (SD) were evaluated in a series of combinations with the real datasets (RD) using the classification task, where the highest accuracy was obtained with the pre-trained Resnet50 model (99,2%), showing that increasing the RD with SD obtained from DCGAN helps to achieve greater accuracy.

Original languageEnglish
Pages (from-to)765-771
Number of pages7
JournalInternational Journal of Advanced Computer Science and Applications
Issue number11
StatePublished - 2020

Bibliographical note

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  • Deep Convolutional Generative Adversaria Network (DCGAN)
  • Generative Adversarial Network (GAN)
  • classification
  • deep learning
  • gastrointestinal parasites


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