Abstract
Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naïve Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naïve Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.
Highlights
In previous years, magnetic resonance imaging (MRI) has been efficiently used to study the functionality of the human brain
This paper presents a framework for identifying defects in fetal brains
The results of the new approach presented in this article indicate a good performance using MRI images
Summary
Magnetic resonance imaging (MRI) has been efficiently used to study the functionality of the human brain. A MRI scan for a fetus provides great details of the soft tissue and the structure of the brain. It could be applied for primary identification of brain abnormalities and tumors with no need for medical interferences [2,3]. It helps doctors to confer an accurate diagnosis, allowing doctors to provide parents with an understanding of the disease in order to prepare them for dealing with the abnormality. It helps in managing the pregnancy and problems that might occur during this period. It improves the quality of diagnosis and aids the decision for the best suitable treatment plan [4]
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