Detection of Anemia Through Nail Images Using Convolutional Neural Networks

Ccaso I.A.; Arocutipa Y.D.; Chiuyari-Veramendi W.N.; Franco Medina J.L.; Apaza Alanoca H. · IEEE Andescon, ANDESCON 2024 - Proceedings (2024) · DOI: 10.1109/andescon61840.2024.10755798

Resumen

At the intersection of medicine and technology, early disease detection has undergone a significant transformation thanks to the crucial role played by image processing and the use of Convolutional Neural Networks (CNN). This study focuses on early detection of anemia by detailed analysis of nail images. In Peru, the prevalence of anemia in children from 6 to 35 months was 43.1 %, it was higher in the rural area (50.3%) than in the urban area (40.2 %), this due to that in some regions the availability of health professionals is limited. By integrating image processing technology with the power of CNNs, an accuracy of 77 % is achieved. This convergence not only improves diagnostic accuracy, but also offers an accurate, accessible and non-invasive solution for early identification of anemia. This innovative approach not only represents a technological advance, but also has the potential to mark a significant milestone in the improvement of healthcare, making it an accessible non-invasive tool for the detection of anemia through nail image analysis.

Instituciones: Universidad Nacional de Cañete; Universidad Nacional de Moquegua