ArticleScientific reports2023
Parasitic egg recognition using convolution and attention network.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Automated detection and classification of intestinal protozoan cysts using a nuclei structure-based deep learning framework for epidemiological surveillance.Parasite epidemiology and control · 2026Article
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Performance validation of deep-learning-based approach in stool examination.Parasites & vectors · 2025Article
- A lightweight deep-learning model for parasite egg detection in microscopy images.Parasites & vectors · 2024Article
- Machine vision-based autonomous road hazard avoidance system for self-driving vehicles.Scientific reports · 2024Article
- Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.Scientific reports · 2024Article
- Automating parasite egg detection: insights from the first AI-KFM challenge.Frontiers in artificial intelligence · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.
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