Anemia is a universal public health issue,which occurs as the result of a reduction in red blood cells.This disease is common among children in Africa and other developing countries.If not treated early,children may s...Anemia is a universal public health issue,which occurs as the result of a reduction in red blood cells.This disease is common among children in Africa and other developing countries.If not treated early,children may suffer longterm consequences such as impairment in social,emotional,and cognitive functioning.Early detection of anemia in children is highly desirable for effective treatment measures.While there has been research into the development of computer-aided diagnosis(CAD)systems for anemia diagnosis,a significant proportion of these studies encountered limitations when working with limited datasets.To overcome the existing issues,this paper proposes a large dataset,named CP-AnemiC,comprising 710 individuals(range of age,6–59 months),gathered from several hospitals in Ghana.The conjunctiva image-based dataset is supported with Hb levels(g/dL)annotations for accurate diagnosis of anemia.A joint deep neural network is developed that simultaneously classifies anemia and estimates hemoglobin levels(g/dL)based on the conjunctival pallor images.This paper conducts a comprehensive experiment on the CP-AnemiC dataset.The experimental results demonstrate the efficacy of the joint deep neural network in both the tasks of anemia classification and Hb levels(g/dL)estimation.展开更多
Anemia is one of the public health issues that affect children and pregnant women globally.Anemia occurs when the level of red blood cells within the body is reduced.Detecting anemia requires expert blood draw for cli...Anemia is one of the public health issues that affect children and pregnant women globally.Anemia occurs when the level of red blood cells within the body is reduced.Detecting anemia requires expert blood draw for clinical analysis of hemoglobin quantity.Although this standard method is accurate,it is costive and consumes enough time,unlike the non-invasive approach which is cost-effective and takes less time.This study focused on pallor analysis and used images of the conjunctiva of the eyes to detect anemia using machine learning techniques.This study used a publicly available dataset of 710 images of the conjunctiva of the eyes acquired with a unique tool that eliminates any interference from ambient light.We combined Convolutional Neural Networks,Logistic Regression,and Gaussian Blur algorithm to develop a conjunctiva detection model and an anemia detection model which runs on a Fast API server connected to a frontend mobile app built with React Native.The developed model was embedded into a smartphone application that can detect anemia by capturing and processing a patient's conjunctiva with a sensitivity of 90%,a specificity of 95%,and an accuracy of 92.50%on average performance in about 50 s.展开更多
文摘Anemia is a universal public health issue,which occurs as the result of a reduction in red blood cells.This disease is common among children in Africa and other developing countries.If not treated early,children may suffer longterm consequences such as impairment in social,emotional,and cognitive functioning.Early detection of anemia in children is highly desirable for effective treatment measures.While there has been research into the development of computer-aided diagnosis(CAD)systems for anemia diagnosis,a significant proportion of these studies encountered limitations when working with limited datasets.To overcome the existing issues,this paper proposes a large dataset,named CP-AnemiC,comprising 710 individuals(range of age,6–59 months),gathered from several hospitals in Ghana.The conjunctiva image-based dataset is supported with Hb levels(g/dL)annotations for accurate diagnosis of anemia.A joint deep neural network is developed that simultaneously classifies anemia and estimates hemoglobin levels(g/dL)based on the conjunctival pallor images.This paper conducts a comprehensive experiment on the CP-AnemiC dataset.The experimental results demonstrate the efficacy of the joint deep neural network in both the tasks of anemia classification and Hb levels(g/dL)estimation.
文摘Anemia is one of the public health issues that affect children and pregnant women globally.Anemia occurs when the level of red blood cells within the body is reduced.Detecting anemia requires expert blood draw for clinical analysis of hemoglobin quantity.Although this standard method is accurate,it is costive and consumes enough time,unlike the non-invasive approach which is cost-effective and takes less time.This study focused on pallor analysis and used images of the conjunctiva of the eyes to detect anemia using machine learning techniques.This study used a publicly available dataset of 710 images of the conjunctiva of the eyes acquired with a unique tool that eliminates any interference from ambient light.We combined Convolutional Neural Networks,Logistic Regression,and Gaussian Blur algorithm to develop a conjunctiva detection model and an anemia detection model which runs on a Fast API server connected to a frontend mobile app built with React Native.The developed model was embedded into a smartphone application that can detect anemia by capturing and processing a patient's conjunctiva with a sensitivity of 90%,a specificity of 95%,and an accuracy of 92.50%on average performance in about 50 s.
