Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders

The increasing rates of neurodevelopmental disorders (NDs) are threatening pregnant women, parents, and clinicians caring for healthy infants and children. NDs can initially start through embryonic development due to several reasons. Up to three in 1000 pregnant women have embryos with brain defects; hence, the primitive detection of embryonic neurodevelopmental disorders (ENDs) is necessary. Related work done for embryonic ND classification is very limited and is based on conventional machine learning (ML) methods for feature extraction and classification processes. Feature extraction of these methods is handcrafted and has several drawbacks. Deep learning methods have the ability to deduce an optimum demonstration from the raw images without image enhancement, segmentation, and feature extraction processes, leading to an effective classification process. This article proposes a new framework based on deep learning methods for the detection of END. To the best of our knowledge, this is the first study that uses deep learning techniques for detecting END. The framework consists of four stages which are transfer learning, deep feature extraction, feature reduction, and classification. The framework depends on feature fusion. The results showed that the proposed framework was capable of identifying END from embryonic MRI images of various gestational ages. To verify the efficiency of the proposed framework, the results were compared with related work that used embryonic images. The performance of the proposed framework was competitive. This means that the proposed framework can be successively used for detecting END.

[1]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Biao Huang,et al.  Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE , 2018, IEEE Transactions on Industrial Informatics.

[3]  M. Sharkas,et al.  Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age , 2019, Brain sciences.

[4]  Yue Shen,et al.  Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems , 2019, Energies.

[5]  Junhao Wen,et al.  Fundus Image Classification Using VGG-19 Architecture with PCA and SVD , 2018, Symmetry.

[6]  Max A. Viergever,et al.  Automatic brain tissue segmentation in fetal MRI using convolutional neural networks , 2019, Magnetic resonance imaging.

[7]  Daniel Rueckert,et al.  A review on automatic fetal and neonatal brain MRI segmentation , 2017, NeuroImage.

[8]  Andreas Savakis,et al.  Distracted Driver Detection: Deep Learning vs Handcrafted Features , 2017 .

[9]  Yi Pan,et al.  Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier , 2019, Neurocomputing.

[10]  Ghassan Hamarneh,et al.  BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment , 2017, NeuroImage.

[11]  Stephen Marshall,et al.  Breast cancer detection using deep convolutional neural networks and support vector machines , 2019, PeerJ.

[12]  Lindsay I. Smith,et al.  A tutorial on Principal Components Analysis , 2002 .

[13]  J. Winn,et al.  Brain , 1878, The Lancet.

[14]  Liang Zheng,et al.  Image Classification base on PCA of Multi-view Deep Representation , 2019, J. Vis. Commun. Image Represent..

[15]  A. Mechelli,et al.  Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications , 2017, Neuroscience & Biobehavioral Reviews.

[16]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[17]  Christos Davatzikos,et al.  A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages , 2017, NeuroImage.

[18]  Hamid R. Tizhoosh,et al.  Deep Barcodes for Fast Retrieval of Histopathology Scans , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[19]  Noureddine Zerhouni,et al.  Deep Learning in the Biomedical Applications: Recent and Future Status , 2019, Applied Sciences.

[20]  Kaizhu Huang,et al.  Reducing and Stretching Deep Convolutional Activation Features for Accurate Image Classification , 2018, Cognitive Computation.

[21]  Amir Sadeghipour,et al.  Artificial intelligence in retina , 2018, Progress in Retinal and Eye Research.

[22]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[23]  Hailong Li,et al.  Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework , 2018, NeuroImage: Clinical.

[24]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[25]  P. Griffiths,et al.  Use of MRI in the diagnosis of fetal brain abnormalities in utero (MERIDIAN): a multicentre, prospective cohort study , 2017, The Lancet.

[26]  N. Bargalló,et al.  Fetal Brain MRI Texture Analysis Identifies Different Microstructural Patterns in Adequate and Small for Gestational Age Fetuses at Term , 2013, Fetal Diagnosis and Therapy.

[27]  Jyoti Islam,et al.  Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks , 2018, Brain Informatics.

[28]  Feng Liu,et al.  Deep Learning and Its Applications in Biomedicine , 2018, Genom. Proteom. Bioinform..

[29]  Massimo Filippi,et al.  Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks , 2018, NeuroImage: Clinical.

[30]  U. Rajendra Acharya,et al.  Application of deep transfer learning for automated brain abnormality classification using MR images , 2019, Cognitive Systems Research.

[31]  Amir Hussain,et al.  Applications of Deep Learning and Reinforcement Learning to Biological Data , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Maha A. Sharkas,et al.  Detecting and Classifying Fetal Brain Abnormalities Using Machine Learning Techniques , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[33]  Tairui Chen,et al.  Going Deeper with Convolutional Neural Network for Intelligent Transportation , 2016 .

[34]  Omar Mohd Rijal,et al.  Lung Disease Classification using GLCM and Deep Features from Different Deep Learning Architectures with Principal Component Analysis , 2018, International Journal of Integrated Engineering.

[35]  M. Rutter,et al.  Neurodevelopmental disorders. , 2017, The lancet. Psychiatry.

[36]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[37]  C. Barnewolt,et al.  Fetal thoracic abnormalities: MR imaging. , 2003, Radiology.

[38]  Vanathi Gopalakrishnan,et al.  A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks , 2018, NeuroImage.

[39]  G. Rattá,et al.  Automatic Quantitative MRI Texture Analysis in Small-for-Gestational-Age Fetuses Discriminates Abnormal Neonatal Neurobehavior , 2013, PloS one.

[40]  R.Siva Shankar,et al.  Fetal head localization and fetal brain segmentation from MRI using the center of gravity , 2016, 2016 International Computer Science and Engineering Conference (ICSEC).

[41]  E. D’Agaro.  Artificial intelligence used in genome analysis studies , 2018 .

[42]  Makoto Yoshizawa,et al.  Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis , 2016, 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).

[43]  Feng Zhou,et al.  A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning , 2018, Pattern Recognit..

[44]  O. Stegle,et al.  Deep learning for computational biology , 2016, Molecular systems biology.

[45]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[46]  Pat Levitt,et al.  Fetal mechanisms in neurodevelopmental disorders. , 2008, Pediatric neurology.

[47]  Yudong Zhang,et al.  AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT , 2012 .