Anemia Detection from Eyes, Palm and Fingernails with Machine Learning Models
Year: 2025
Type: article
Abstract: This literature review provides a comprehensive examination of non-invasive methods for detecting anemia using advanced machine learning (ML) models, with a focus on analyzing images of hands, palms, and fingernails. Anemia, a prevalent global health issue, particularly affects vulnerable groups such as children and pregnant women. Traditional diagnostic methods, while accurate, are often invasive and less accessible in resource-limited settings, creating the need for alternative approaches. By synthesizing current research, this review explores various ML techniques, including Convolutional Neural Networks (CNNs) and ensemble learning methods, assessing their accuracy and reliability in diagnosing anemia based on image analysis. A unique aspect of this research is the use of smartphone technology for capturing images, making the diagnostic process more accessible, user-friendly, and cost-effective. The findings underscore the promise of non-invasive ML-based approaches for detecting anemia, particularly in underserved populations, but also reveal significant gaps in current research. These include the need for larger, more diverse datasets and improved algorithms that can enhance diagnostic precision and adapt to real-world conditions. While existing models, ranging from conventional machine learning to more advanced neural networks, have shown considerable improvement, further development is necessary for effective real-time testing and application. By leveraging advancements in image processing and ML, this review highlights the potential for these technologies to offer timely medical interventions, improving health outcomes for millions affected by anemia worldwide
Authors: A.S.Nousir, Aya.M.Attia, M. Salama, A. Mostafa, M. Osama, H. A. Khalil, B. Mohamed, Dina Darwish
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7091545309
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