research-article

Entropy Slicing Extraction and Transfer Learning Classification for Early Diagnosis of Alzheimer Diseases with sMRI

Authors:

S. Sambath Kumar,

M. NandhiniAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 2

Article No.: 40, Pages 1 - 22

https://doi.org/10.1145/3383749

Published: 21 April 2021 Publication History

Abstract

Alzheimer’s Disease (AD) is an irreversible neurogenerative disorder that undergoes progressive decline in memory and cognitive function and is characterized by structural brain Magnetic Resonance Images (sMRI). In recent years, sMRI data has played a vital role in the evaluation of brain anatomical changes, leading to early detection of AD through deep networks. The existing AD problems such as preprocessing complexity and unreliability are major concerns at present. To overcome these, a model (FE_ESC_TL) has been proposed with an entropy slicing for feature extraction and Transfer Learning for classification. In the present study, the entropy image slicing method is attempted for selecting the most informative MRI slices during training stages. The ADNI dataset is trained on Transfer Learning adopted by VGG-16 network for classifying the AD with normal individuals. The experimental results reveal that the proposed model has achieved an accuracy level of 93.05%, 86.39%, 92.00% for binary classifications (AD/MCI, MCI/CN, AD/CN) and 93.12% for ternary classification (AD/MCI/CN), respectively, and henceforth the efficiency in diagnosing AD is proved through comparative analysis.

References

[1]

A. Agarwal, S. Negahban, and M. J. Wainwright. 2012. A simple way to prevent neural networks from overfitting. Ann. Stat. 40, 2 (2012), 1171--1197.

[2]

Ane Alberdi, Asier Aztiria, and Adrian Basarab. 2016. On the early diagnosis of Alzheimer’s disease from multimodal signals: A survey. Artif. Intell. Med. 71 (2016), 1--29.

Digital Library

[3]

John Ashburner and Karl J. Friston. 2000. Voxel-based morphometry—The methods. Neuroimage 11, 6 (2000), 805--821.

[4]

Alzheimer’s Association. 2019. 2019 Alzheimer’s disease facts and figures. Alzh. Dement. 15, 3 (2019), 321--387.

[5]

Iman Beheshti, Hasan Demirel, Farnaz Farokhian, Chunlan Yang, Hiroshi Matsuda, Alzheimer’s Disease Neuroimaging Initiative, et al. 2016. Structural MRI-based detection of Alzheimer’s disease using feature ranking and classification error. Comput. Meth. Prog. Biomed. 137 (2016), 177--193.

[6]

Bo Cheng, Mingxia Liu, Daoqiang Zhang, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al. 2019. Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease. Brain Imag. Behav. 13, 1 (2019), 138--153.

[7]

Pierrick Coupé, Simon F. Eskildsen, José V. Manjón, Vladimir S. Fonov, Jens C. Pruessner, Michèle Allard, D. Louis Collins, Alzheimer’s Disease Neuroimaging Initiative, et al. 2012. Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. NeuroIm.: Clin. 1, 1 (2012), 141--152.

[8]

Rémi Cuingnet, Emilie Gerardin, Jérôme Tessieras, Guillaume Auzias, Stéphane Lehéricy, Marie-Odile Habert, Marie Chupin, Habib Benali, Olivier Colliot, Alzheimer’s Disease Neuroimaging Initiative, et al. 2011. Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage 56, 2 (2011), 766--781.

[9]

Maisa Daoud and Michael Mayo. 2019. A survey of neural network-based cancer prediction models from microarray data. Artif. Intell. Med. 97 (2019).

[10]

Xiaohong W. Gao and Rui Hui. 2016. A deep learning based approach to classification of CT brain images. In Proceedings of the SAI Computing Conference (SAI’16). IEEE, 28--31.

[11]

Serge Gauthier, Barry Reisberg, Michael Zaudig, Ronald C. Petersen, Karen Ritchie, Karl Broich, Sylvie Belleville, Henry Brodaty, David Bennett, Howard Chertkow, et al. 2006. Mild cognitive impairment. The Lancet 367, 9518 (2006), 1262--1270.

[12]

Kasthurirangan Gopalakrishnan, Siddhartha K. Khaitan, Alok Choudhary, and Ankit Agrawal. 2017. Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Construct. Build. Mater. 157 (2017), 322--330.

[13]

K. A. N. N. P. Gunawardena, R. N. Rajapakse, and N. D. Kodikara. 2017. Applying convolutional neural networks for pre-detection of Alzheimer’s disease from structural MRI data. In Proceedings of the 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP’17). IEEE, 1--7.

[14]

Haruo Hanyu, Tomohiko Sato, Kentaro Hirao, Hidekazu Kanetaka, Toshihiko Iwamoto, and Kiyoshi Koizumi. 2010. The progression of cognitive deterioration and regional cerebral blood flow patterns in Alzheimer’s disease: A longitudinal SPECT study. J. Neurolog. Sci. 290, 1--2 (2010), 96--101.

[15]

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.

[16]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In Proceedings of the IEEE International Conference on Computer Vision. 1026--1034.

Digital Library

[17]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.

[18]

Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, Alzheimer’s Disease Neuroimaging Initiative, et al. 2019. Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network. Front. Neurosci. 13 (2019), 509.

[19]

Clifford R. Jack Jr, Matt A. Bernstein, Nick C. Fox, Paul Thompson, Gene Alexander, Danielle Harvey, Bret Borowski, Paula J. Britson, Jennifer L. Whitwell, Chadwick Ward, et al. 2008. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imag.: Offic. J. Int. Society Magn. Reson. Med. 27, 4 (2008), 685--691.

[20]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[21]

Igor Kononenko. 2001. Machine learning for medical diagnosis: History, state of the art and perspective. Artif. Intell. Med. 23, 1 (2001), 89--109.

Digital Library

[22]

K. R. Kruthika, H. D. Maheshappa, Alzheimer’s Disease Neuroimaging Initiative, et al. 2019. Multistage classifier-based approach for Alzheimer’s disease prediction and retrieval. Inf. Med. Unlock. 14 (2019), 34--42.

[23]

S. Sambath Kumar and M. Nandhini. 2019. Analysis of surface-based morphometric of hippocampal subfield volumetry in Alzheimer’s disease and MCI. Institute of Integrative Omics and Applied Biotechnology 10, 1 (2019), 21--26.

[24]

Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278--2324.

[25]

Baiying Lei, Siping Chen, Dong Ni, and Tianfu Wang. 2016. Discriminative learning for Alzheimer’s disease diagnosis via canonical correlation analysis and multimodal fusion. Front. Aging Neurosci. 8 (2016), 77.

[26]

Fan Li, Manhua Liu, Alzheimer’s Disease Neuroimaging Initiative, et al. 2018. Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks. Comput. Med. Imag. Graph. 70 (2018), 101--110.

[27]

Hongming Li, Mohamad Habes, and Yong Fan. 2017. Deep ordinal ranking for multi-category diagnosis of Alzheimer’s disease using hippocampal MRI data. arXiv preprint arXiv:1709.01599 (2017).

[28]

Xingjuan Li, Yu Li, and Xue Li. 2017. Predicting clinical outcomes of Alzheimer’s disease from complex brain networks. In Proceedings of the International Conference on Advanced Data Mining and Applications. Springer, 519--525.

[29]

Manhua Liu, Danni Cheng, Kundong Wang, Yaping Wang, Alzheimer’s Disease Neuroimaging Initiative, et al. 2018. Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics 16, 3--4 (2018), 295--308.

[30]

Siqi Liu, Sidong Liu, Weidong Cai, Hangyu Che, Sonia Pujol, Ron Kikinis, Dagan Feng, Michael J. Fulham, et al. 2014. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62, 4 (2014), 1132--1140.

[31]

Siyuan Lu, Zhihai Lu, and Yu-Dong Zhang. 2019. Pathological brain detection based on AlexNet and transfer learning. J. Comput. Sci. 30 (2019), 41--47.

[32]

Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. 2013. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the International Conference on Machine Learning, Vol. 30. 3.

[33]

Eleanor A. Maguire, David G. Gadian, Ingrid S. Johnsrude, Catriona D. Good, John Ashburner, Richard S. J. Frackowiak, and Christopher D. Frith. 2000. Navigation-related structural change in the hippocampi of taxi drivers. Proc. Nat. Acad. Sci. 97, 8 (2000), 4398--4403.

[34]

Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning. 807--814.

[35]

Jeffrey S. Phillips, Fulvio Da Re, Laynie Dratch, Sharon X. Xie, David J. Irwin, Corey T. McMillan, Sanjeev N. Vaishnavi, Carlo Ferrarese, Edward B. Lee, Leslie M. Shaw, et al. 2018. Neocortical origin and progression of gray matter atrophy in nonamnestic Alzheimer’s disease. Neurobiol. Aging 63 (2018), 75--87.

[36]

Fabio Previtali, Paola Bertolazzi, Giovanni Felici, and Emanuel Weitschek. 2017. A novel method and software for automatically classifying Alzheimer’s disease patients by magnetic resonance imaging analysis. Comput. Meth. Prog. Biomed. 143 (2017), 89--95.

Digital Library

[37]

Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. 2011. ORB: An efficient alternative to SIFT or SURF. In Proceedings of the International Conference on Computer Vision. IEEE, 2564--2571.

Digital Library

[38]

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. 2015. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 3 (2015), 211--252.

Digital Library

[39]

David H. Salat, Jeffrey A. Kaye, and Jeri S. Janowsky. 1999. Prefrontal gray and white matter volumes in healthy aging and Alzheimer disease. Arch. Neurol. 56, 3 (1999), 338--344.

[40]

Daniel Schmitter, Alexis Roche, Bénédicte Maréchal, Delphine Ribes, Ahmed Abdulkadir, Meritxell Bach-Cuadra, Alessandro Daducci, Cristina Granziera, Stefan Klöppel, Philippe Maeder, et al. 2015. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer’s disease. NeuroIm.: Clin. 7 (2015), 7--17.

[41]

Heung-Il Suk, Seong-Whan Lee, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al. 2014. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101 (2014), 569--582.

[42]

Heung-Il Suk, Chong-Yaw Wee, and Dinggang Shen. 2013. Discriminative group sparse representation for mild cognitive impairment classification. In Proceedings of the International Workshop on Machine Learning in Medical Imaging. Springer, 131--138.

Digital Library

[43]

Muhammed Talo, Ulas Baran Baloglu, Özal Yıldırım, and U. Rajendra Acharya. 2019. Application of deep transfer learning for automated brain abnormality classification using MR images. Cog. Syst. Res. 54 (2019), 176--188.

[44]

K. B. Walhovd, A. M. Fjell, J. Brewer, L. K. McEvoy, C. Fennema-Notestine, D. J. Hagler, R. G. Jennings, D. Karow, A. M. Dale, Alzheimer’s Disease Neuroimaging Initiative, et al. 2010. Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease. Amer. J. Neuroradiol. 31, 2 (2010), 347--354.

[45]

Guorong Wu, Minjeong Kim, Gerard Sanroma, Qian Wang, Brent C. Munsell, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al. 2015. Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. NeuroImage 106 (2015), 34--46.

[46]

Tingting Ye, Chen Zu, Biao Jie, Dinggang Shen, Daoqiang Zhang, Alzheimer’s Disease Neuroimaging Initiative, et al. 2016. Discriminative multi-task feature selection for multi-modality classification of Alzheimer’s disease. Brain Imag. Behav. 10, 3 (2016), 739--749.

[47]

Daoqiang Zhang, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al. 2012. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59, 2 (2012), 895--907.

[48]

Daoqiang Zhang, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al. 2012. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS One 7, 3 (2012).

[49]

Daoqiang Zhang, Yaping Wang, Luping Zhou, Hong Yuan, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al. 2011. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55, 3 (2011), 856--867.

[50]

Ke Zhou, Wenguang He, Yonghui Xu, Gangqiang Xiong, and Jie Cai. 2018. Feature selection and transfer learning for Alzheimer’s disease clinical diagnosis. Appl. Sci. 8, 8 (2018), 1372.

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Index Terms

Entropy Slicing Extraction and Transfer Learning Classification for Early Diagnosis of Alzheimer Diseases with sMRI
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 2

May 2021

410 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3461621

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2021 ACM.

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Association for Computing Machinery

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Publication History

Published: 21 April 2021

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 January 2020

Received: 01 October 2019

Published in TOMM Volume 17, Issue 2

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Cited By

Bibi NCourtney JMcGuinness K(2025)Enhancing Brain Disease Diagnosis with XAI: A Review of Recent StudiesACM Transactions on Computing for Healthcare10.1145/3709152Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3709152
Zhang HLiu MQi YYang NHu SNie LZhang W(2024)Efficient Brain Tumor Segmentation with Lightweight Separable Spatial Convolutional NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365371520:7(1-19)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3653715
Xu XCao GSong TKang G(2024)Adversarial Transfer Learning for Alzheimer's Disease Diagnosis Using Structural MRIProceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering10.1145/3637732.3637775(45-53)Online publication date: 28-Feb-2024
https://doi.org/10.1145/3637732.3637775
Mu SShan SLi LJing SLi RZheng CCui X(2024)DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer’s Disease ClassificationIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.339864032(1955-1964)Online publication date: 2024
https://doi.org/10.1109/TNSRE.2024.3398640
Zhang WYang XChen YLiu Y(2024)Alzheimer's Disease Classification Based on Multi-Scale 2D-VMD Swin Transformer2024 9th International Conference on Computer and Communication Systems (ICCCS)10.1109/ICCCS61882.2024.10603331(178-183)Online publication date: 19-Apr-2024
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Kaur AMittal MBhatti JThareja SSingh S(2024)A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's diseaseArtificial Intelligence in Medicine10.1016/j.artmed.2024.102928154:COnline publication date: 1-Aug-2024
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Toumaj SHeidari AShahhosseini RJafari Navimipour N(2024)Applications of deep learning in Alzheimer’s disease: a systematic literature review of current trends, methodologies, challenges, innovations, and future directionsArtificial Intelligence Review10.1007/s10462-024-11041-558:2Online publication date: 20-Dec-2024
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Liu HFei ZZhou WFei M(2024)Deep Learning Models in Early Diagnosis of Alzheimer’s Disease: A Systematic Review of Current Applications and ChallengesAdvancement in Computational Methods for Life Systems Modelling and Simulation10.1007/978-981-96-0188-2_23(289-303)Online publication date: 28-Dec-2024
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