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Open AccessArticle

Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age

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Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt
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Department of Computer and Communication Engineering (SSP), Faculty of Engineering, Alexandria University, Alexandria 21526, Egypt
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Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(9), 231; https://doi.org/10.3390/brainsci9090231
Received: 21 August 2019 / Revised: 30 August 2019 / Accepted: 8 September 2019 / Published: 12 September 2019
(This article belongs to the Section Neuroimaging)
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. View Full-Text
Keywords: biomedical image processing; ensemble classification; fetal brain abnormalities; principal component analysis (PCA); feature extraction biomedical image processing; ensemble classification; fetal brain abnormalities; principal component analysis (PCA); feature extraction
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Attallah, O.; Sharkas, M.A.; Gadelkarim, H. Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age. Brain Sci. 2019, 9, 231.

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