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您的检索式：作者名="Stephen Afrifa"
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| 1 | Detection of anemia using conjunctiva images:A smartphone application approach显示文摘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. | Peter Appiahene Enoch Justice Arthur Stephen Korankye Stephen Afrifa Justice Williams Asare Emmanuel Timmy Donkoh | 2023 | Medicine in Novel Technology and Devices2023，，2： | 1 |
| 2 | Application of ensemble models approach in anemia detection using images of the palpable palm显示文摘Anemia is a public health issue with serious ramifications for human health globally.Anemia particularly affects pregnant women and children from 6 to 59 months old even though every individual is at risk.Anemia occurs when the Hb level is below its normal threshold or when the red blood cells are weakened or destroyed.To discover medical remedies on time,early detection or diagnosis of anemia assist patients to understand their condition.The invasive approach for anemia detection is costive and time-consuming as compared to the non-invasive approach which is reliable and suitable for developing communities where medical resources and personnel are inadequate.This study uses palpable palm images(dataset)collected from 710 participants in selected hospitals in Ghana.The images were extracted,segmented and converted into RGB percentile to train,validate and tested the machine learning models.A hybrid model was developed with the application of ensemble learning models using the R programming language on the R Studio platform.Stacking,voting,boosting and bagging ensemble model techniques were used to build the hybrid models,the stacking ensemble model achieved an accuracy of 99.73%.The study justifies that ensemble models are efficient for medical disease diagnosis or detection such as anemia. | Peter Appiahene Samuel Segun Dzifa Dogbe Emmanuel Edem Yaw Kobina Philip Sackey Dartey Stephen Afrifa Emmanuel Timmy Donkoh Justice Williams Asare | 2023 | Medicine in Novel Technology and Devices2023，，4： | 0 |
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