Machine vision model using nail images for non-invasive detection of iron deficiency anemia in university students

Machine vision model using nail images for non-invasive detection of iron deficiency anemia in university students

IntroductionIron deficiency anemia (IDA) is a global health issue that significantly affects quality of life. Non-invasive methods, such as image analysis using artificial vision, offer accessible alternatives for diagnosis. This study proposes a DenseNet169-based model to detect anemia from nail im...

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Bibliographic Details
Main Authors: Jorge Raul Navarro-Cabrera, Miguel Angel Valles-Coral, María Elena Farro-Roque, Nelly Reátegui-Lozano, Lolita Arévalo-Fasanando
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Big Data
Subjects:
cross validation
data capture
DenseNet169
deep learning
non-invasive diagnostics
Online Access:https://www.frontiersin.org/articles/10.3389/fdata.2025.1557600/full
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https://www.frontiersin.org/articles/10.3389/fdata.2025.1557600/full

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