Design and implimentation of a Pre-Eclampsia detection system using image processing.
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Preeclampsia (toxaemia) is a complex disease of pregnancy with sometimes serious effects on maternal and infant morbidity and mortality. It is defined by hypertension after 20 weeks’ gestation and proteinuria. Preeclampsia affects from 5% to 14% of all pregnant women and is responsible for about 14% of maternal deaths per year in the world. This project focuses on the use of image processing techniques to design a low- cost system for local health centers capable of early detection of preeclampsia in women at risk. The major objective of the project was to design and implement a low-cost pre- eclampsia detection system using image processing techniques for urine analysis. This system carries out analysis and diagnosis using blood pressure readings as well as urine samples since presence of protein (albumin) in the urine is a major indicator of preeclampsia. The image processing feature is deployed in analyzisng dipped test strips whose colour indicates the presence and level of protein (albumin). We were able to design and implement a low-cost preeclampsia detection system. The diagnostic system uses a Raspberry-Pi microprocessor and a camera. This system runs an application/program into which pressure readings can be entered and using a picture of a urine-dipped test strip, is able to carry out detection and diagnosis of pre- eclampsia. Future improvements would be use of a higher resolution camera for increased precision during diagnosis, as well as creation of a fully self-integrated system.