Discovering Senile Dementia from Brain MRI Using Ra-DenseNet

With the rapid development of medical industry, there is a growing demand for disease diagnosis using machine learning technology. The recent success of deep learning brings it to a new height. This paper focuses on application of deep learning to discover senile dementia from brain magnetic resonance imaging (MRI) data. In this work, we propose a novel deep learning model based on Dense convolutional Network (DenseNet), denoted as ResNeXt Adam DenseNet (Ra-DenseNet), where each block of DenseNet is modified using ResNeXt and the adapter of DenseNet is optimized by Adam algorithm. It compresses the number of the layers in DenseNet from 121 to 40 by exploiting the key characters of ResNeXt, which reduces running complexity and inherits the advantages of Group Convolution technology. Experimental results on a real-world MRI data set show that our Ra-DenseNet achieves a classification accuracy with 97.1\(\%\) and outperforms the existing state-of-the-art baselines (i.e., LeNet, AlexNet, VGGNet, ResNet and DenseNet) dramatically.

[1]  Daoqiang Zhang,et al.  Ensemble sparse classification of Alzheimer's disease , 2012, NeuroImage.

[2]  Dinggang Shen,et al.  A novel relational regularization feature selection method for joint regression and classification in AD diagnosis , 2017, Medical Image Anal..

[3]  Xiaohua Hu,et al.  Exploring matrix factorization techniques for significant genes identification of Alzheimer’s disease microarray gene expression data , 2011, BMC Bioinformatics.

[4]  Lei Zhang,et al.  Salt restriction: Recognition and treatment of chronic kidney disease related edema in ancient literature mining , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[5]  Dinggang Shen,et al.  Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification , 2014, NeuroImage.

[6]  Thomas E. Nichols,et al.  Best practices in data analysis and sharing in neuroimaging using MRI , 2017, Nature Neuroscience.

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

[8]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Xuejun Yang,et al.  A algorithm for identifying disease genes by incorporating the subcellular localization information into the protein-protein interaction networks , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[10]  Daoqiang Zhang,et al.  Multimodal classification of Alzheimer's disease and mild cognitive impairment , 2011, NeuroImage.

[11]  Heikki Huttunen,et al.  Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects , 2015, NeuroImage.

[12]  Seyed-Ahmad Ahmadi,et al.  Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound , 2016, Comput. Vis. Image Underst..

[13]  Marcia Hon,et al.  Towards Alzheimer's disease classification through transfer learning , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[14]  Daoqiang Zhang,et al.  Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease , 2012, NeuroImage.

[15]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[16]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[17]  V. Calhoun,et al.  Selective changes of resting-state networks in individuals at risk for Alzheimer's disease , 2007, Proceedings of the National Academy of Sciences.

[18]  Qiao Liu,et al.  VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in genome-wide association studies: Applied to Alzheimer's disease , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[19]  Geoffrey E. Hinton,et al.  On the importance of initialization and momentum in deep learning , 2013, ICML.

[20]  Yan Yang,et al.  Analysis of senile dementia from the brain magnetic resonance imaging data with clustering , 2018, Data Science and Knowledge Engineering for Sensing Decision Support.

[21]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[23]  Xiaohua Hu,et al.  Exploring matrix factorization techniques for significant genes identification of microarray dataset , 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[24]  Liang Chen,et al.  Multi-modal classification of Alzheimer's disease using nonlinear graph fusion , 2017, Pattern Recognit..

[25]  M. Jorge Cardoso,et al.  Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment☆ , 2013, NeuroImage: Clinical.

[26]  Junming Shao,et al.  Based on the Network Degeneration Hypothesis: Separating Individual Patients with Different Neurodegenerative Syndromes in a Preliminary Hybrid PET/MR Study , 2016, The Journal of Nuclear Medicine.

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

[28]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[29]  Daniel Rueckert,et al.  Random forest-based similarity measures for multi-modal classification of Alzheimer's disease , 2013, NeuroImage.

[30]  Gang Chen,et al.  Multi-label learning by exploiting label correlations for TCM diagnosing Parkinson's disease , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[31]  D. Harman,et al.  Alzheimer's Disease Pathogenesis , 2006, Annals of the New York Academy of Sciences.

[32]  John G. Csernansky,et al.  Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.

[33]  Armando Barreto,et al.  A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).