Kedar, D.S.Pandey, G.Koolagudi, S.G.2026-02-0620252025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings, 2025, Vol., , p. 718-724https://doi.org/10.1109/AIDE64228.2025.10987403https://idr.nitk.ac.in/handle/123456789/28681Anemia, characterized by low levels of red blood cells or hemoglobin, affects millions worldwide, significantly affecting public health. Traditional diagnostic methods, while effective, are invasive, costly, and inaccessible in resource-constrained settings. This paper proposes a non-invasive approach for anemia detection using conjunctival images analyzed through deep learning techniques. The proposed methodology involves capturing high-resolution conjunctival images, pre-processing them, and using a customized Convolutional Neural Network (CNN) for feature extraction and classification. The results achieved by the customized CNN fine-tuned with a batch size of 16 give an Accuracy of 96%, Precision of 95%, Recall of 96%, and ROC-AUC score of 0.99. The customized CNN outperformed the other models for this work, such as Random Forest, XGBoost, SVM, ResNet50, and MobileNetV2. This work highlights the potential for non-invasive diagnostic tools to improve accessibility and efficiency in healthcare, particularly for underserved populations. The findings endorse integrating deep learning in healthcare as a transformative approach to address global challenges such as anemia. © 2025 IEEE.AnemiaConjunctivalConvolutional Neural NetworkNon-invasiveRandom Forest ClassifierXGBoost