Detection of anemia can be done by examining the hemoglobin concentration level in the blood using complete blood count, which is an invasive, time‐consuming, and costly technique. Preliminary methods for detecting anemia include examining the color of the palpebral conjunctiva, which is a non‐invasive method, but color perception may vary from person to person. This study aims to develop a computerized non‐invasive technique for anemia detection. We propose a novel machine learning model using the artificial neural network to detect anemic patients from the images of eye conjunctiva. Since limited and small dataset has been used in the earlier approaches, this may cause over fitting of the model. We have improved the number of available training images using image augmentation techniques. To standardize a non‐invasive method, we have used computer vision algorithms for preprocessing and feature extraction. This article derives the backpropagation rules mathematically for adjusting the weights for the proposed neural network model. After hyper parameter tuning and using the mathematically derived backpropagation rules, the model was able to achieve the best accuracy of 97.00% with sensitivity 99.21% and specificity 95.42% on the created dataset.

中文翻译：

---

基于神经网络的非侵入性方法从眼结膜图像中检测贫血

贫血的检测可以通过使用全血计数检查血液中的血红蛋白浓度水平来完成，这是一种侵入性、耗时且昂贵的技术。检测贫血的初步方法包括检查睑结膜的颜色，这是一种非侵入性方法，但颜色感知可能因人而异。本研究旨在开发一种用于贫血检测的计算机化非侵入性技术。我们提出了一种新的机器学习模型，使用人工神经网络从眼结膜图像中检测贫血患者。由于在早期方法中使用了有限的小数据集，这可能会导致模型过度拟合。我们使用图像增强技术改进了可用训练图像的数量。为了标准化非侵入性方法，我们使用计算机视觉算法进行预处理和特征提取。本文从数学上推导出反向传播规则，用于调整所提出的神经网络模型的权重。在超参数调整和使用数学推导的反向传播规则后，该模型能够在创建的数据集上达到 97.00% 的最佳准确率、99.21% 的灵敏度和 95.42% 的特异性。