Detecting and Classifying Fetal Brain Abnormalities Using Machine Learning Techniques

Abstract

Detecting and classifying fetal brain abnormalities from magnetic resonance imaging (MRI) is important, as approximately 3 in 1000 women are pregnant with a fetal of abnormal brain. Early detection of fetal brain abnormalities using machine learning techniques can improve the quality of diagnosis and treatment planning. The literature has shown that most of the work made to classify brain abnormalities in very early age is for preterm infants and neonates not fetal. However, research papers that studied fetal brain MRI images have mapped these images with the neonates MRI images to classify an abnormal behavior in newborns not fetal. In this paper, a pipeline process is proposed for fetal brain classification (FBC) which uses machine learning techniques. The main contribution of this paper is the classification of fetal brain abnormalities in early stage, before the fetal is born. The proposed algorithm is capable of detecting and classifying a variety of abnormalities from MRI images with a wide range of fetal gestational age (GA) (from 16 to 39 weeks) using a flexible and simple method with low computational cost. The novel proposed method consists of four phases; segmentation, enhancement, feature extraction and classification. The results have shown that the proposed method has an area under ROC curve (AUC) of 84%, 86%, 80% and 84.5% for, Linear discriminate analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN), and Ensemble Subspace Discriminates classifiers respectively. This shows that our proposed has successfully classified fetal brain abnormalities with images of different fetal GA. The results are promising. Future work will be done to improve classification results and increase the dataset.