Development of fuzzy approach to predict the fetus safety and growth using AFI

Nowadays, prediction of abnormality plays a vital role in healthcare applications for deciding the treatment and guiding for proper treatment on time. The amniotic fluid is the water of the womb, and it is a strong indicator of congenital fetal anomaly. The automatic calculation of amniotic fluid index (AFI) and shape features of varying gestational periods will be useful to predict the perinatal outcome of high risk in maternity patients. Some perinatal outcomes are expected fetal weight, head circumferences and need of new-born ICU which decide the mode of delivery. These perinatal outcomes will be helpful in increasing the live birth and reducing the risk of premature delivery. The aim of this work is to identify the abnormal AFI of expectant mothers to alert the clinicians. Computer-aided diagnosis supports the clinicians in decision-making process. In the proposed work, using the training set of ultrasound images, the shape templates are developed by using deformable methods. Contour points in the edges will be helpful to find the AFI. After that, features are extracted and fuzzy logic algorithm is used to classify the given image into one of the four categories such as oligohydramnios, borderline, normal and hydramnios state for expectant mothers and their impact on fetal growth. The outcome of the proposed approach is measured in two different ways. The first outcome is that calculated AFI will be compared with the value calculated by the radiologist/clinicians, and the second outcome is that along with AFI, shape feature with contour points and gestational age are used for making decision/classification such as normal, borderline, oligohydramnios and hydramnios, and the classified results will also be compared with the expert’s opinion. The outcomes are represented quantitatively. The results proved that AFI calculated by the proposed work was matching 94% with the expert opinion and classification of test image into any one of the categories such as normal, borderline, oligohydramnios and hydramnios fetched average accuracy of prediction up to 92.5%.