Recognition and Classification of Fetal Brain Abnormalities

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E. Saraswathi, et. al.

Abstract

In today’s world, not only adults, children, teenage suffer from different diseases but also the yet to be born babies suffer from various abnormalities. We hear many cases that the child was born with some disability and because of the delay in the discovery and operation of the disability, the disability becomes permanent. Out of one thousand, three women are pregnant with abnormal child. If somehow, we detect the abnormality in the child when it is in the fetal stage and operate and medicate according to that then the abnormality can be treated very effectively and efficiently. Our paper deals with the same concept of detecting and classifying the brain abnormalities in the fetus using various deep learning techniques and algorithms. There have been previous works on the same but the technique used by others included machine learning and it had some drawbacks which can be resolved using deep learning techniques. Deep learning has greater efficiency and advantage over machine learning. In our method of detection, we take the help of MRI (Magnetic Resonance Imaging) technique to first capture the brain image of the fetus. Then we perform various preprocessing steps to extract the ROI (Region of Interest). Then we use feature extraction and reduction techniques to obtain more developed and detailed image of the fetus. We compare the image with the normal fetal brain images to classify and detect abnormalities. We make the use of CNN (Convolutional Neural Network) classifier algorithm of the deep learning technique to achieve high level of accuracy. CNN algorithm is better than k means clustering and SVM classifier algorithm of machine learning techniques. Our work has shown higher accuracy than previous models and our future work will involve increasing of classification and data.

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