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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0111193
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 11, 2020.
Abstract: 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.
Keywords: Generative Adversarial Network (GAN); Deep Con-volutional Generative Adversaria Network (DCGAN); gastrointesti-nal parasites; classification; deep learning
Mila Yoselyn Pacompia Machaca, Milagros Lizet Mayta Rosas, Eveling Castro-Gutierrez, Henry Abraham Talavera Diaz and Victor Luis Vasquez Huerta, “Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111193
@article{Machaca2020,
title = {Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111193},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111193},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {Mila Yoselyn Pacompia Machaca and Milagros Lizet Mayta Rosas and Eveling Castro-Gutierrez and Henry Abraham Talavera Diaz and Victor Luis Vasquez Huerta}
}
