Deep Learning Approaches for Anemia Diagnosis through Classification Techniques

A. Deenu Mol, S. Subashini, S. Karthikkumar, P. Hrithikkumar, K. Mohammed Ashraf, S. Kavin Prabhu

2025

Abstract

A non-invasive method for anemia detection via nail images was developed in this study based on deep learning and machine learning approaches. Data were acquired and preprocessed in this study, including augmentation and normalization to improve model performance. Feature extraction was conducted with the Inception v3 model and then classified using the Random Forest and Support Vector Machine algorithms. This is an effective way to predict anemia with less cost and time than conventional blood tests. The performance evaluation was done by using accuracy and confusion matrix in which promising results were achieved in detecting non-invasive anemia. The combination of deep learning with Random Forest and SVM gives a scalable solution with the most advantages in resource-poor areas.

Paper Citation

in Harvard Style

Mol A., Subashini S., Karthikkumar S., Hrithikkumar P., Ashraf K. and Prabhu S. (2025). Deep Learning Approaches for Anemia Diagnosis through Classification Techniques. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 826-831. DOI: 10.5220/0013921400004919

in Bibtex Style

@conference{icrdicct`2525,
author={A. Mol and S. Subashini and S. Karthikkumar and P. Hrithikkumar and K. Ashraf and S. Prabhu},
title={Deep Learning Approaches for Anemia Diagnosis through Classification Techniques},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={826-831},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013921400004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Deep Learning Approaches for Anemia Diagnosis through Classification Techniques
SN - 978-989-758-777-1
AU - Mol A.
AU - Subashini S.
AU - Karthikkumar S.
AU - Hrithikkumar P.
AU - Ashraf K.
AU - Prabhu S.
PY - 2025
SP - 826
EP - 831
DO - 10.5220/0013921400004919
PB - SciTePress