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.
Highlights
Diseases caused by parasites are a public health problem on a global scale; they can be of high risk and high prevalence, as shown by their incidence rates in the population
The best accuracy: 0.992 was obtained with the Resnet50 model, and the dataset: synthetic datasets (SD) obtained after applying the Wiener filter + the real datasets (RD)
Deep Convolutional Adversarial Generative Networks (DCGAN) was used to increase synthetic data, and the results generated were compared through the classification www.ijacsa.thesai.org
Summary
Diseases caused by parasites are a public health problem on a global scale; they can be of high risk and high prevalence, as shown by their incidence rates in the population. According to the National Institute of Health of Peru (INS), intestinal parasitism increased its prevalence rate among sectors with fewer resources [3]. Methods based on deep learning that have had excellent results in similar applications require large datasets. The application of two techniques for image enhancement is presented, Wiener and Wavelet, in order to obtain an improvement in the quality of the images; an approach to increase data is presented for the generation of synthetic training samples of microscopy images of eight species of gastrointestinal parasites, using DCGAN a variation of GAN [5], from a reduced initial dataset. The article’s structure is explained below: In Section II, related works are addressed, Section III describes the methodology used, to finish with the results and conclusions in Sections IV and V, respectively
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