Application of Machine Learning in Detecting Iron Deficiency Anemia Using Conjunctiva image Dataset from Ghana

Anemia is a global public health issue that mostly emerges as a result of a decrease in red blood cell count and is particularly prevalent in Africa. Invasive ways of detecting anemia are expensive and time-consuming. Anemia may, however, be diagnosed using non-invasive technologies such as machine learning algorithms. In our study, we compared machine learning models to detect anemia using the conjunctiva of the eyes. The main datasets consisting of the conjunctiva of the eyes were acquired using Ghana as a case study for dataset collecting. Before the study began, the ethical committees at the hospitals involved approved the collection of datasets. Also, because the participants (patients) in the study were minors, the ethical agreement was obtained from their parent(s) or guardian(s), and the purpose and objectives of the study were explained to them, along with the advantages of the health services. Before the participants were enrolled in the data collection, their parent(s) or guardian(s) gave their consent. Furthermore, the ethics and consent committee of the University of Energy and Natural Resources, Ghana approved the start of this experiment. Furthermore, patients' or participants' names and faces were not shown or exposed during image capture, rendering their identification unknown贫血是全球性公共卫生问题，主要由红细胞计数减少引发，在非洲地区尤为高发。有创贫血检测手段不仅成本高昂，且耗时冗长。不过，借助机器学习算法等无创技术，可实现贫血的无创诊断。本研究中，我们对比了多款基于眼结膜图像的贫血检测机器学习模型。本研究以加纳为数据集采集案例，获取了核心的眼结膜图像数据集。研究启动前，所有参与医院的伦理委员会均已批准数据集采集工作。鉴于本研究的受试者（患者）均为未成年人，我们已获取其父母或监护人的伦理同意，并向其详尽讲解了研究的目的、目标以及医疗服务的相关获益。在受试者参与数据采集前，其父母或监护人已签署正式知情同意书。此外，加纳能源与自然资源大学伦理与知情同意委员会已批准本实验的开展。同时，图像采集过程中未展示或泄露受试者的姓名与面部信息，确保其身份无法被识别。