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Detecting and Classifying Fetal Brain Abnormalities Using Machine Learning Techniques

机译：使用机器学习技术对胎儿脑异常进行检测和分类

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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.
机译：由于磁共振成像（MRI）对胎儿脑部异常的检测和分类很重要，因为大约每千名女性中就有3名怀孕的胎儿患有脑部异常。使用机器学习技术及早发现胎儿脑部异常可以提高诊断和治疗计划的质量。文献表明，在很早的时候就对脑部异常进行分类的大部分工作是针对早产儿和新生儿而不是胎儿。但是，研究胎儿大脑MRI图像的研究论文已将这些图像与新生儿MRI图像进行映射，以对非胎儿新生儿的异常行为进行分类。在本文中，提出了一种使用机器学习技术进行胎儿脑分类（FBC）的流水线过程。本文的主要贡献是在胎儿出生前早期对胎儿脑部异常进行分类。所提出的算法能够以灵活，简单的方法以低计算成本从具有宽范围胎龄（GA）（16至39周）的MRI图像中检测和分类各种异常。提出的新方法包括四个阶段。分割，增强，特征提取和分类。结果表明，对于线性判别分析（LDA），支持向量机（SVM），K近邻（ KNN）和Ensemble Subspace分别区分分类器。这表明我们的建议已成功地使用不同胎儿GA的图像对胎儿脑部异常进行了分类。结果是有希望的。将来将进行工作以改善分类结果并增加数据集。

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