Performance Analysis of Squeezenet and Densenet on Fetal Brain MRI Dataset

Abstract

Monitoring fetal growth is an essential activity throughout gestation period. It is necessary for the well-being of the mother and the child so that any abnormalities can be diagnosed at the earlier stage itself and preventive measures can be taken. MRI Image analysis is necessary to identify the anomalies that have a higher probability of occurrence during the first and second trimesters. Fetal brain MRI images are obtained and analysed for any disorder in the neurodevelopment of the embryo. Classification of the fetal brain image into normal and abnormal cases is done using deep learning algorithms like densenet121 and squeezenet1_0. Densenet produced an accuracy of 98.7% when compared to squeezenet whose accuracy is 96.3%. The performance of densenet is high in case of various metrics like precision, recall, sensitivity and F1 Score.