Abstract
The classification of Alzheimer’s disease (AD) using ADNI dataset requires suitable feature segmenting techniques to detect the existing and relevant finer smaller brain region features, together with effective classification model, to eliminate a massive, labor-intensive and time-consuming voxel-based morphometry technique. Here, in this paper, a deep learning-based segmenting method using SegNet to detect AD pertinent brain parts features from structural magnetic resonance imaging (sMRI) and subsequently classifying accurately AD and dementia condition using ResNet-101 is presented. A deep learning-based image segmenting approach is experimented in detecting the delicate features of brain morphological changes due to AD that benefits classification performance for cognitive normal, mild cognitive impairment and AD, and thus provides an easy automatic diagnosis of Alzheimer’s diseases. For classification, ResNet-101 is trained applying features extracted from SegNet with ADNI dataset. This paper demonstrated particularly to attain top-level automated classification. The seven morphological features like grey matter, white matter, cortex surface, gyri and sulci contour, cortex thickness, hippocampus and cerebrospinal fluid space extracted from 240 sMRI with SegNet are used to train ResNet for classification, and this classifier achieved a sensitivity of 96% and an accuracy of 95% over 240 ADNI sMRI other than used for training.
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Braak, H.; Braak, E.; Bohl, J.; Bratzke, H.: Evolution of Alzheimer’s disease related cortical lesions. In: Gertz, H.-J., Arendt, Th (eds.) Alzheimer’s Disease-from Basic Research to Clinical Applications, pp. 97–106. Springer, Vienna (1998)
Bain, L.J.; Jedrziewski, K.; Morrison-Bogorad, M.; Albert, M.; Cotman, C.; Hendrie, H.; et al.: Healthy brain aging: a meeting report from the Sylvan M. Cohen Annual Retreat of the University of Pennsylvania Institute on Aging. Alzheimer’s Dement. 4, 443–446 (2008)
Alzheimer’s Association: 2016 Alzheimer’s disease facts and figures, Alzheimer’s and Dementia, 12(4), pp. 459–509 (2016)
Wattmo, C.; Londos, E.; Minthon, L.: Risk factors that affect life expectancy in Alzheimer’s disease: a 15-year follow-up. Dement. Geriatr. Cogn. Disord. 38, 286–299 (2014)
Dubois, B.; Feldman, H.H.; Jacova, C.; DeKosky, S.T.; Barberger-Gateau, P.; Cummings, J.; et al.: Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 6, 734–746 (2007)
Albert, M.S.; DeKosky, S.T.; Dickson, D.; Dubois, B.; Feldman, H.H.; Fox, N.C.; et al.: The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the national institute on aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 7, 270–279 (2011)
Petersen, R.C.; Caracciolo, B.; Brayne, C.; Gauthier, S.; Jelic, V.; Fratiglioni, L.: Mild cognitive impairment: a concept in evolution. J. Int. Med. 275, 214–228 (2014)
Artero, S.; Petersen, R.; Touchon, J.; Ritchie, K.: Revised criteria for mild cognitive impairment: validation within a longitudinal population study. Dement. Geriatr. Cogn. Disord. 22, 465–470 (2006)
Petersen, R.C.: Mild cognitive impairment as a diagnostic entity. J. Int. Med. 256, 183–194 (2004)
Wee, C.-Y.; Yap, P.-T.; Shen, D.; For the Alzheimer’s disease Neuroimaging Initiative: Prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns. Human Brain Mapping. 34, 3411–3425 (2013)
Cuingnet, R.; Gerardin, E.; Tessieras, J.; Auzias, G.; Leheéricy, S.; Habert, M.-O.; et al.: Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage. 56, 766–781 (2011)
Hanyu, H.; Sato, T.; Hirao, K.; Kanetaka, H.; Iwamoto, T.; Koizumi, K.: The progression of cognitive deterioration and regional cerebral blood flow patterns in Alzheimer’s disease: a longitudinal SPECT study. J. Neurol. Sci. 290(1–2), 96–101 (2010)
Gray, K.R.; Wolz, R.; Heckemann, R.A.; Aljabar, P.; Hammers, A.; Rueckert, D.: Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease. NeuroImage 60(1), 221–229 (2012)
Liu, F.; Zhou, L.; Shen, C.; Yin, J.: Multiple kernel learning in the primal for multi-modal Alzheimer’s disease classification. IEEE J. Biomed. Health Inform. 18(3), 984–990 (2014)
Zhang, D.; Wang, Y.; Zhou, L.; Yuan, H.; Shen, D.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55(3), 856–867 (2011)
Papakostas, G.A.; Savio, A.; Graña, M.; Kaburlasos, V.G.: A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data. Neurocomputing 150, 37–42 (2015)
Beheshti, I.; Demirel, H.: Probability distribution function-based classification of structural MRI for the detection of Alzheimer’s disease. Comput. Biol. Med. 64, 208–216 (2015)
Moradi, E.; Pepe, A.; Gaser, C.; Huttunen, H.; Tohka, J.: Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. NeuroImage 104, 398–412 (2015)
Bron, E.E.; Smits, M.; Vrenken, H.; Barkhof, F.; Scheltens, P.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CAD dementia challenge. NeuroImage 111, 562–579 (2015)
Zhang, Y.; Dong, Z.; Phillips, P.; et al.: Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front. Comput. Neurosci. 9, 66 (2015)
Andersen, A.H.; Rayens, W.S.; Liu, Y.; Smith, C.D.: Partial least squares for discrimination in fMRI data. Magn. Reson. Imaging 30(3), 446–452 (2012)
Mesrob, L.: DTI and structural MRI classification in Alzheimer’s disease. Adv. Mol. Imag. 02, 12–20 (2012)
Lee, W.; Park, B.; Han, K.: Classification of diffusion tensor images for the early detection of Alzheimer’s disease. Comput. Biol. Med. 43(10), 1313–1320 (2013)
Diamantaras, K.I.; Kung, S.Y.: Principal Component Neural Networks. Wiley, New York (1996)
Barnes, J.; Bartlett, J.W.; van de Pol, L.A.; et al.: A meta-analysis of hippocampal atrophy rates in Alzheimer’s disease. Neurobiol. Aging 30(11), 1711–1723 (2009)
Magnin, B.; Mesrob, L.; Kinkingnéhun, S.; et al.: Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Diagnost. Neuroradiol. 51(2), 73–83 (2009)
Fjell, A.M.; Walhovd, K.B.; Fennema-Notestine, C.; et al.: CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer’s disease. J. Neurosci. 30(6), 2088–2101 (2010)
Liu, S.; Liu, S.; Cai, W.; et al.: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015)
Liu, S.; Liu, S.; Cai, W.; Pujol, S.; Kikinis, R.; Feng, D.: Early diagnosis of Alzheimer’s disease with deep learning. In: Proceedings/IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1015–1018 (2014)
Li, F.; Tran, L.; Thung, K.H.; Ji, S.; Shen, D.; Li, J.: A robust deep model for improved classification of AD/MCI patients. IEEE J. Biomed. Health Inform. 19(5), 1610–1616 (2015)
Ye, J.; Wu, T.; Li, J.; Chen, K.: Machine learning approaches for the neuroimaging study of Alzheimer’s disease. IEEE Comput. 4(4), 99–101 (2011)
Rama, R. K.; Park, H. C.; Lee, S.-W.: Sparse feature selection using import vector machines for classification of Alzheimer’s disease. In: Proceedings of 2016 King Fall Conference (2016)
Zhu, X.; Suk, H.; Wang, L.; Lee, S.W.; Shena, D.: Alzheimer’s Disease Neuroimaging Initiative A Novel Relational Regularization Feature Selection Method for Joint Regression and Classification in AD Diagnosis, Medical Image Analysis. Elseiver, Amsterdam (2015)
Xu, L.; Wu, X.; Li, R.; Chen, K.; Long, Z.; Zhang, J.; et al.: Prediction of progressive mild cognitive impairment by multi-modal neuroimaging biomarkers. J. Alzheimer’s Dis. 51, 1045–1056 (2016)
Gönen, M.; Alpaydin, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211–2268 (2011)
Wu, X.; Li, Q.; Xu, L.; Chen, K.; Yao, L.: Multi-feature kernel discriminant dictionary learning for face recognition. Pattern Recogn. 66, 404–411 (2017)
Vieira, S.; Pinaya, W.H.; Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders. Methods and applications. Neurosci. Biobehav. Rev. 74, 58–75 (2017)
He, K.; Zhang, X.; Ren, S.; Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 770-778 (2016)
Simonyan, K.; Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv: 1409.1556 (2014)
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A.C.; Fei-Fei, L.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115, 211–252 (2015)
Long, J.; Shelhamer, E.; Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
Liang-Chieh, C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: ICLR (2015)
Ioffe, S.; Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR, arXiv:1502.03167 (2015)
Badrinarayanan, V.; Kendall, A.; Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for scene segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Zhang, A.; Lipton, Z.C. ; Li, M.; Smola, A.J.: Dive into Deep Learning Release 0.7.1. https://d2l.ai/d2l-en.pdf (2020)
Wu, H.; Xin, M.; Fang, W.; Hu, H.M.; Hu, Z.: Multi-level feature network with multi-loss for person re-identification. IEEE Access (2015)
Schaer, M.; Cuadra, M.B.; Schmansky, N.; Fischl, B.; Thiran, J.P.; Eliez, S.: How to measure cortical folding from MR images: a step-by-step tutorial to compute local gyrification index. J. Vis. Exp. 59, e3417 (2012)
Lu, D.; Popuri, K.; Ding, W.; Balachandar, R.; Faisal, M.: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MRI and FDG-pPET images. Sci. Rep 8, 1–13 (2018)
Lian, C.; Liu, M.; Zhang, J.; Shen, D.: Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Trans. Pattern Anal. Mach. Intell. 42, 880–893 (2020)
Acknowledgements
The sMRI dataset was collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) http://adni.loni.usc.edu/data-samples/access-data/.
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Buvaneswari, P.R., Gayathri, R. Deep Learning-Based Segmentation in Classification of Alzheimer’s Disease. Arab J Sci Eng 46, 5373–5383 (2021). https://doi.org/10.1007/s13369-020-05193-z
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DOI: https://doi.org/10.1007/s13369-020-05193-z