文章基本信息

标题：Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification
其他标题：Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification
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作者：Mila Yoselyn Pacompia Machaca ; Milagros Lizet Mayta Rosas ; Eveling Castro-Gutierrez 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：11
DOI：10.14569/IJACSA.2020.0111193
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Generative Adversarial Network (GAN); Deep Con-volutional Generative Adversaria Network (DCGAN); gastrointesti-nal parasites; classification; deep learning