ArticleBioMed research international2025
Constructing a Predictive Model for STH and Schistosomiasis Classification From Microscopic Images.
Article in BioMed research international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Constructing a Predictive Model for STH and Schistosomiasis Classification From Microscopic Images.BioMed research international · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Soil-transmitted helminths (STHs) and schistosomiasis are widespread parasitic diseases in tropical regions, particularly in Africa, with substantial health and socioeconomic burdens. Early diagnosis and treatment are critical for mitigating these impacts. Conventional microscopy-based diagnosis was time-consuming and labor-intensive, posing challenges in resource-limited settings such as Ethiopia. This study developed an innovative system that combined machine learning (ML) and deep learning to analyze microscope images of parasite eggs, improving diagnostic speed and accuracy compared to traditional CNN-only approaches. We compared a hybrid CNN-ML approach with standalone deep learning models and vision transformers (ViTs) for classifying five categories:
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.