Published Paper Details:

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 a very early age is for preterm infants and neonates not fetuses. However, research papers that studied fetal brain MRI images have mapped these images with the neonates MRI images to classify an abnormal behaviour in newborns not fetal. In this work, a pipeline process is proposed for fetal brain classification (FBC) which uses machine learning techniques. The main contribution of this work 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; pre-processing, segmentation, feature extraction and classification. In the pre-process, the input image is converted into gray scale and apply the wiener filter for image enhancement. After pre-processing, the adaptive segmentation is applied for segmenting the image. Then DWT and statistical features are extracted from segmented images. Finally, the input image is classified using the Decision Tree algorithm.