research-article

The Effect of Smoothing Filter on CNN based AD Classification

Authors:

Shuicai WuAuthors Info & Claims

ICBBS '19: Proceedings of the 2019 8th International Conference on Bioinformatics and Biomedical Science

Pages 54 - 58

https://doi.org/10.1145/3369166.3369191

Published: 13 January 2020 Publication History

Abstract

Gaussian smoothing (GS) is a spatial low pass filtering method widely used in neuroimaging preprocessing. Full width at half maximum (FWHM) is a common parameter when the imaging data convolved with GS kernel. The convolutional neural networks (CNNs) can be considered as the feature extractor, which is implemented by applying a series of different filters. However, the influence of kernel size of GS for feature extraction remains unclear. In this study, we describe an automatic AD classification algorithm that is built on a pre-trained CNN model, AlexNet for feature extraction and support vector machine (SVM) for classification. The algorithm was trained and tested using the structural Magnetic Resonance Imaging (sMRI) data from Alzheimer's Disease Neuroimaging Initiative (ADNI). The data used in this study include 191 Alzheimer's disease (AD) patients and 103 normal control (NC) subjects. We evaluate the influence of FWHM on classification performance. When FWHM is 0mm, the classification accuracy obtained the highest value for AD and NC, which reached 91.5%, 92.4%, 89.0% for conv3, conv4 and conv5 of AlexNet respectively. The classification accuracy at each layer is relatively low when FWHM is 8mm. The result suggests that the higher smooth value may have a negative effect on feature extraction of CNNs during AD classification.

References

[1]

Liu, S., Cai, W., Liu, S., Zhang, F., Fulham, M., Feng, D., Kikinis, R. 2015. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Informatics. 2, 3 (Sep. 2015), 167--180. DOI= http://dx.doi.org/10.1007/s40708-015-0019-x.

[2]

Ruan, Q., D'Onofrio, G., Sancarlo, D., Bao, Z., Greco, A., and Yu, Z. 2016. Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer's disease: a systematic review. BMC Geriatr. 16, 1 (May. 2016), 104.

[3]

Ashburner, J., and Friston, K. J. 2000. Voxel-Based Morphometry-The Methods. Neuroimage. 11, 6 (Jun. 2000), 805--821.

[4]

Jones, Derek K., Mark R. Symms, Mara Cercignani, and Robert J. Howard. 2005. The effect of filter size on VBM analyses of DT-MRI data. Neuroimage. 26, 2 (Jun. 2005), 546--554.

[5]

Angermueller, C., Pärnamaa, T., Parts, L., and Stegle, O. 2016. Deep learning for computational biology. Mol. Syst. Biol. 12, 7 (Jul. 2016), 878.

[6]

Tian, M., Lan, L., Zhang, B.W., and Wu, S.C. Study on the application of deep learning in neuroimaging. China Medical Devices. 31, 12 (Dec. 2016), 4--9.

[7]

Liu, S., Liu, S., Cai, W., Che, H. and Fulham, M. J. 2015. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Transactions on Biomedical Engineering. 62, 4 (Apr. 2015), 1132--1140.

[8]

Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D. 2014. Early diagnosis of Alzheimer's disease with deep learning. 2014 IEEE 11th international symposium on biomedical imaging (ISBI). (Apr. 2014), 1015--1018.

[9]

Zhang, B.W., Lan, L., and Wu, S.C. Application of deep learning to mild cognitive impairment conversion and classification. Chinese Medical Equipment Journal. 38, 9 (Sep. 2017), 105--111.

[10]

Lan, L., and ZHANG, B.W. 2018. MCI Conversion Prediction Based on Transfer Learning. DEStech Transactions on Computer Science and Engineering(CCNT). 218--222

[11]

Yue, L., Gong, X., Chen, K., Mao, M., Li, J., Nandi, A. K., and Li, M. 2018. Auto-Detection of Alzheimer's Disease Using Deep Convolutional Neural Networks. 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). (Jul. 2018), 228--234.

[12]

Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., and Liu, E. 2013. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimer's & Dementia. 9, 5 (Sep. 2013), e111-e194.

[13]

Busatto, G. F., Garrido, G. E., Almeida, O. P., Castro, C. C., Camargo, C. H., Cid, C. G., and Bottino, C. M. 2003. A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer's disease. Neurobiol. Aging. 24, 2 (Mar.-Apr. 2003), 221--231.

[14]

Ashburner, J. 2009. Computational anatomy with the SPM software. Magn. Reson. Imaging. 27, 8 (Oct. 2009), 1163--1174.

[15]

Ashburner, J. 2007. A fast diffeomorphic image registration algorithm. Neuroimage. 38, 1 (Oct. 2007), 95--113.

[16]

Wolk, D. A., Das, S. R., Mueller, S. G., Weiner, M. W., Yushkevich, P. A., and Initiative, A. s. D. N. 2017. Medial temporal lobe subregional morphometry using high resolution MRI in Alzheimer's disease. Neurobiol. aging. 49 (Jan. 2017), 204--213.

[17]

Zhang, B.W., Lan, L., Sun S., and Wu, S.C. Alzheimer's disease diagnosis model based on deep convolutional neural network. Chinese Medical Equipment Journal. 40, 1 (Jan. 2019), 5--9.

[18]

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., and Darrell, T. 2014. Caffe: Convolutional architecture for fast feature embedding. The 22nd ACM international conference on Multimedia (Orlando, Florida, USA, November 03-07, 2014). ACM. New York, NY, 675--678.

Digital Library

[19]

Krizhevsky, A., Sutskever, I., and Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 25, 2 (Jan. 2012), 1079--1105.

[20]

Hotelling, H. 1933. Analysis of a complex of statistical variables into principal components. Journal of educational psychology. 24, 6, 417--441.

[21]

Steyerberg, E. W., Eijkemans, M. J., and Habbema, J. D. F. 1999. Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. Journal of clinical epidemiology. 52, 10 (Oct. 1999), 935--942.

[22]

Chang, C. C., and Lin, C. J. 2011. LIBSVM: A library for support vector machines. ACM transactions on intelligent systems and technology (TIST), ACM Trans. Intell. Syst. Technol. 2, 3, Article 27 (April 2011), 27.

Digital Library

Cited By

Agarwal DMarques Gde la Torre-Díez IFranco Martin MGarcía Zapiraín BMartín Rodríguez F(2021)Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic ReviewSensors10.3390/s2121725921:21(7259)Online publication date: 31-Oct-2021
https://doi.org/10.3390/s21217259
Kim ACha HKim EKim SLee HPark ELee YJung TChang Y(2021)Impact of fractional amplitude of low‐frequency fluctuations in motor‐ and sensory‐related brain networks on spinal cord injury severityNMR in Biomedicine10.1002/nbm.461235:1Online publication date: 10-Sep-2021
https://doi.org/10.1002/nbm.4612

Index Terms

The Effect of Smoothing Filter on CNN based AD Classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Classification of sMRI for Alzheimer's disease Diagnosis with CNN: Single Siamese Networks with 2D+? Approach and Fusion on ADNI
ICMR '17: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval

The methods of Content-Based visual information indexing and retrieval penetrate into Healthcare and become popular in Computer-Aided Diagnostics. The PhD research we have started 13 months ago is devoted to the multimodal classification of MRI brain ...
Efficient deep CNN-based gender classification using Iris wavelet scattering
Abstract
Recognition of gender from iris images can be considered a texture classification task in which a classification model discriminates iris textures of male and female subjects. Although many researchers have proposed efficient iris texture ...
Classification and Feature Selection of Alzheimer’s Disease for MRI Data Utilizing Convolutional Neural Network and Support Vector Machine
Abstract
Alzheimer's disease (AD) is a neurological disease that affect numerous people. According to the literature, forecasting this type of disease can be an extremely difficult process. Machine Learning and Artificial Intelligence play a significant ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICBBS '19: Proceedings of the 2019 8th International Conference on Bioinformatics and Biomedical Science

October 2019

141 pages

ISBN:9781450372510

DOI:10.1145/3369166

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Beijing University of Technology
Harbin Inst. Technol.: Harbin Institute of Technology

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 January 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICBBS 2019

ICBBS 2019: 2019 8th International Conference on Bioinformatics and Biomedical Science

October 23 - 25, 2019

Beijing, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
99
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Agarwal DMarques Gde la Torre-Díez IFranco Martin MGarcía Zapiraín BMartín Rodríguez F(2021)Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic ReviewSensors10.3390/s2121725921:21(7259)Online publication date: 31-Oct-2021
https://doi.org/10.3390/s21217259
Kim ACha HKim EKim SLee HPark ELee YJung TChang Y(2021)Impact of fractional amplitude of low‐frequency fluctuations in motor‐ and sensory‐related brain networks on spinal cord injury severityNMR in Biomedicine10.1002/nbm.461235:1Online publication date: 10-Sep-2021
https://doi.org/10.1002/nbm.4612

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents