Anemia Detection using Palpable Palm Image Datasets from Ghana

Published: 27 June 2022| Version 1 | DOI: 10.17632/ccr8cm22vz.1
Justice Williams Asare,


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 colour using the palpable palm. The main dataset consisting of the palm were developed using Ghana as a case study for dataset collecting, and the approach employed is as follows. All photographs were taken by the laboratory personnel, and the Hb Values of patients, age, sex, and a remark (anemic or non-anemic) based on the Hb Value taken were uploaded by technicians or medical laboratory officers using standard high-quality cameras with a minimum of 12MP. The lab officers hold the tip of the fingers and wrist to expose the palm because the participants are minors and cannot completely extend the palm to the expectancy. Furthermore, to minimize inflated shine effects caused by the picture quality, which greatly impacts detection or classification by the models, the cameras' spotlights were turned off when the photos were photographed. This approach is an excellent way to eliminate the impact of ambient light on photos in datasets. The datasets were collected from the undermentioned hospitals located in Ghana; Komfo Anokye Teaching Hospital at Kumasi, Bolgatanga Regional Hospital at Bolgatanga, Kintampo Municipal Hospital at Kintampo, Ahmadiyya Muslim Hospital at Techiman, Sunyani Municipal Hospital at Sunyani, Manhyia District Hospital at Kumasi, Ejusu Government Hospital at Ejusu, SDA Hospital at Sunyani, Nkawie-Toase Government Hospital at Nkawie-Toase and Holy Family Hospital at Berekum. The dataset for this study focuses on young children aged five and below. Laboratory technicians take a blood sample from suspected anemic patients to measure the Hb values of the patients, and images of the palpable palm are taken afterwards, and a remark is given based on the Hb value of the patient either anemic or non-anemic. The threshold triangle approach was used to extract the ROI of the pictures of the palpable palm; that is, ROI differentiation for binary after the background was generated was inferred in the dataset collecting procedure. Following that, the images were classified as anemic or non-anemic based on the corresponding comments provided by the laboratory Hb result. In the procedure of augmenting the images to expand the original size of the datasets, from 710 images (426 are anemic images while 284 are non-anemic images) to 4260 datasets after the image augmentation of because of the augmentation, all photos were labelled "anemic" and "non-anemic," with a number matching to each. The reason that all participants are under the age of six, images of their palm may be tiny, as compared to that of adults’ images of the palm.



Machine Learning