Editorial | DOI: https://doi.org/10.31579/2690-4861/1073
1 Department of Public Health and Institute of Biomedical Sciences, Faculty of Health Sciences, University of Carabobo, Venezuela.
2 Rafael María Baralt National Experimental University, Venezuela.
*Corresponding Author: Gilberto Bastidas, Department of Public Health and Institute of Biomedical Sciences, Faculty of Health Sciences, University of Carabobo, Venezuela.
Citation: Bastidas Gilberto, Bastidas-Delgado Gilberto de Jesús, (2026), Images in the Automatic Detection and Classification of Parasites, International Journal of Clinical Case Reports and Reviews, 35(5); DOI:10.31579/2690-4861/1073
Copyright: © 2026, Gilberto Bastidas. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Received: 15 April 2026 | Accepted: 06 May 2026 | Published: 21 May 2026
Keywords:
low- and middle-income countries in Asia, South America, and Africa. Of all reported cases, 357 million are caused by protozoa, especially Cryptosporidium spp., Entamoeba histolytica, and Giardia duodenalis, and 1.4 billion by helminths, including Ascaris lumbrocoides, Trichuris trichiura, Necator americanus, and Ancylostoma duodenale. Furthermore, parasites are responsible for 2.9 million disabilities and more than 40 million deaths annually, distributed among the wide variety of parasites. In the intestinal tract alone, more than 100 species have been reported to affect humans [1-4]. Furthermore, the clinical manifestations of parasitic infections are broad and varied, and in some cases, there are no obvious symptoms. This complicates diagnosis despite the development of new diagnostic techniques (protein and nucleic acid determinations), as examination with an optical microscope remains the gold standard even in the 21st century, despite its inherent biases related to sensitivity, economic and physical effort, availability, competence, and the commitment of the expert or professional [5-7]. Consequently, the automatic detection and classification of parasites using deep learning models can be exponentially enhanced, as evidenced by the pre-trained convolutional neural network (which allows for the adjustment of different layers) and the addition of transfer learning. Deep learning, a component of machine learning, focuses on the structure and function of the human brain, specifically its neural network, for data processing in various fields of knowledge, including parasitology [8, 9]. Each neural layer, based on algorithms in deep learning, a field whose use has expanded due to the exponential growth of data (deep learning includes neural networks of the following types: deep feedback, convolutional, recurrent, and self-encoding), performs an evaluation based on the information it receives. These evaluations are always different from those of the other layers in the tasks of classification, processing, extraction, selection, and feature learning in the images used for diagnosing different species of parasites capable of affecting humans [10, 11]. The accuracy, speed, and flexibility of deep learning allow for its application in the automatic detection of parasites. Feature segmentation is a key aspect of information processing, particularly important for identifying parasite genera with multiple species in the same infection, as seen in malaria. This is based on the detection of individual parasites or groups of similar parasites, supported by various tasks of machine learning algorithms [12-14]. It can therefore be firmly believed that deep learning models substantially improve the diagnosis of parasitic diseases, particularly those that focus their diagnosis on microscopic images, despite the difficulties described in the execution of these models in relation to images with artifacts, data interpretation, small-scale training, overfitting, uncertainty, and disappearance gradient.
The authors have no conflict of interest to declare. The authors declared that this study has received no financial support.
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