survey

A Comprehensive Report on Machine Learning-based Early Detection of Alzheimer's Disease using Multi-modal Neuroimaging Data

Authors:

Pravat Kumar MandalAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 55, Issue 2

Article No.: 43, Pages 1 - 44

https://doi.org/10.1145/3492865

Published: 14 March 2022 Publication History

Abstract

Alzheimer's Disease (AD) is a devastating neurodegenerative brain disorder with no cure. An early identification helps patients with AD sustain a normal living. We have outlined machine learning (ML) methodologies with different schemes of feature extraction to synergize complementary and correlated characteristics of data acquired from multiple modalities of neuroimaging. A variety of feature selection, scaling, and fusion methodologies along with confronted challenges are elaborated for designing an ML-based AD diagnosis system. Additionally, thematic analysis has been provided to compare the ML workflow for possible diagnostic solutions. This comprehensive report adds value to the further advancement of computer-aided early diagnosis system based on multi-modal neuroimaging data from patients with AD.

Supplementary Material

sharma (sharma.zip)

Supplemental movie, appendix, image and software files for, A Comprehensive Report on Machine Learning-based Early Detection of Alzheimer's Disease using Multi-modal Neuroimaging Data

Download
81.46 KB

CONFLICT OF INTEREST

We agree with the content and declare no conflicts of interest in this study.

Acknowledgment

Besides, we are obliged to HelpAge India for the participation of healthy old volunteers in the ongoing studies of the NINS laboratory at the National Brain Research Center and also thank the relatives of the patients for their immense help.

References

[1]

A. S. Association. 2018. Alzheimer's disease facts and figures. Alzheimer's & Dementia 14, 3 (2018), 367–429.

[2]

M. Xia and Y. He. 2011. Magnetic resonance imaging and graph theoretical analysis of complex brain networks in neuropsychiatric disorders. Brain Connectivity 1, 5 (2011), 349–365.

[3]

F. Bernier, P. Kumar, Y. Sato, and Y. J. A. S. D. C. F. T. F. Oda. 2015. Recent progress in the identification of non-invasive biomarkers to support the diagnosis of Alzheimer's disease in clinical practice and to assist human clinical trials 225, 2015.

[4]

A. M. Kälin et al. 2017. Subcortical shape changes, hippocampal atrophy and cortical thinning in future Alzheimer's disease patients. (in English), Original Research 9, 38 (2017).

[5]

N. Chow et al. 2012. Comparing hippocampal atrophy in Alzheimer's dementia and dementia with Lewy bodies 34, 1 (2012), 44–50.

[6]

N. Sheikh-Bahaei, S. A. Sajjadi, R. Manavaki, and J. H. J. J. O. A. S. D. R. Gillard. 2017. Imaging biomarkers in Alzheimer's disease: A practical guide for clinicians 1, 1 (2017), 71–88.

[7]

C. J. T. I. B. Humpel. 2011. Identifying and validating biomarkers for Alzheimer's disease 29, 1 (2011), 26–32.

[8]

R. Sperling. 2011. The potential of functional MRI as a biomarker in early Alzheimer's disease. Neurobiology of Aging 32 (2011), S37–S43.

[9]

G. C. Schwindt and S. E. Black. 2009. Functional imaging studies of episodic memory in Alzheimer's disease: A quantitative meta-analysis. Neuroimage 45, 1 (2009), 181–190.

[10]

F. U. Fischer, D. Wolf, A. Scheurich, A. Fellgiebel, and A. S. D. N. I. J. N. Clinical. 2015. Altered whole-brain white matter networks in preclinical Alzheimer's disease 8 (2015), 660–666.

[11]

P. S. Weston, I. J. Simpson, N. S. Ryan, S. Ourselin, and N. C. Fox. 2015. Diffusion imaging changes in grey matter in Alzheimer's disease: A potential marker of early neurodegeneration. Alzheimer's Research & Therapy 7, 1 (2015), 47.

[12]

G. Stebbins and C. J. B. N. Murphy. 2009. Diffusion tensor imaging in Alzheimer's disease and mild cognitive impairment 21, 1 2 (2009), 39–49.

[13]

J. L. D. da Rocha, I. Bramati, G. Coutinho, F. T. Moll, and R. J. S. R. Sitaram. 2020. Fractional anisotropy changes in parahippocampal cingulum due to Alzheimer's disease 10, 1 (2020), 1–8.

[14]

P. K. Mandal, S. Saharan, M. Tripathi, and G. Murari. 2015. Brain glutathione levels–a novel biomarker for mild cognitive impairment and Alzheimer's disease. Biological Psychiatry 78, 10 (2015), 702–710.

[15]

P. K. Mandal, H. Akolkar, and M. Tripathi. 2012. Mapping of hippocampal pH and neurochemicals from in vivo multi-voxel 31P study in healthy normal young male/female, mild cognitive impairment, and Alzheimer's disease. Journal of Alzheimer's Disease 31, s3 (2012), S75–S86.

[16]

A. Coutinho et al. 2015. Analysis of the posterior cingulate cortex with [18 F] FDG-PET and Naa/mI in mild cognitive impairment and Alzheimer's disease: Correlations and differences between the two methods 9, 4 (2015), 385–393.

[17]

D. Shukla, P. K. Mandal, M. Tripathi, G. Vishwakarma, R. Mishra, and K. J. H. B. M. Sandal. 2020. Quantitation of in vivo brain glutathione conformers in cingulate cortex among age-matched control, MCI, and AD patients using MEGA-PRESS 41, 1 (2020), 194–217.

[18]

C. Promteangtrong et al. 2015. Multimodality imaging approaches in Alzheimer's disease. Part II: 1H MR Spectroscopy, FDG PET and Amyloid PET 9, 4 (2015), 330–342.

[19]

S. Saharan and P. K. J. J. O. A. S. D. Mandal. 2014. The emerging role of glutathione in Alzheimer's disease 40, 3 (2014), 519–529.

[20]

Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan. 2015. Chest pathology detection using deep learning with non-medical training. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) 2015, 294–297: IEEE.

[21]

M. H. Hesamian, W. Jia, X. He, and P. Kennedy. 2019. Deep learning techniques for medical image segmentation: achievements and challenges. Journal of Digital Imaging 32, 4 (2019), 582–596.

[22]

S. Sharma and R. Mehra. 2020. Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images—a comparative insight. Journal of Digital Imaging 33, 3 (2020), 632–654.

[23]

S. Sharma, R. Mehra, and S. Kumar. 2021. Optimised CNN in conjunction with efficient pooling strategy for the multi-classification of breast cancer. IET Image Processing 15, 4 (2021), 936–946.

[24]

Y. Wang and T. Liu. 2015. Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker. Magnetic Resonance in Medicine 73, 1 (2015), 82–101.

[25]

H. Jang et al. 2017. Correlations between gray matter and white matter degeneration in pure Alzheimer's disease, pure subcortical vascular dementia, and mixed dementia. Scientific Reports 7, 1 (2017), 1–9.

[26]

P. M. Thompson et al. 2003. Dynamics of gray matter loss in Alzheimer's disease. Journal of Neuroscience 23, 3 (2003), 994–1005.

[27]

J. Liu et al. 2017. Altered functional connectivity in patients with post‑stroke memory impairment: A resting fMRI study. Experimental and Therapeutic Medicine 14, 3 (2017), 1919–1928.

[28]

Q. Feng et al. 2018. Corpus callosum radiomics-based classification model in Alzheimer's disease: A case-control study 9, (2018) 618.

[29]

K. Strimbu and J. A. Tavel. 2010. What are biomarkers? Current Opinion in HIV and AIDS 5, 6 (2010), 463.

[30]

B. C. Dickerson and R. A. J. N. Sperling. 2005. Neuroimaging biomarkers for clinical trials of disease-modifying therapies in Alzheimer's disease 2, 2 (2005), 348–360.

[31]

S. Lehericy et al. 1994. Amygdalohippocampal MR volume measurements in the early stages of Alzheimer disease. American Journal of Neuroradiology 15, 5 (1994), 929–937.

[32]

H. Braak and E. J. A. n. Braak. 1991. Neuropathological stageing of Alzheimer-related changes 82, 4 (1991), 239–259.

[33]

M. R. Sabuncu et al. 2011. The dynamics of cortical and hippocampal atrophy in Alzheimer disease 68, 8 (2011), 1040–1048.

[34]

J. C. Pruessner, D. L. Collins, M. Pruessner, and A. C. J. J. O. N. Evans. 2001. Age and gender predict volume decline in the anterior and posterior hippocampus in early adulthood 21, 1 (2001), 194–200.

[35]

N. Raz et al. 2005. Regional brain changes in aging healthy adults: General trends, individual differences and modifiers 15, 11 (2005), 1676–1689.

[36]

K. M. Rodrigue and N. J. J. O. N. Raz. 2004. Shrinkage of the entorhinal cortex over five years predicts memory performance in healthy adults 24, 4 (2004), 956–963.

[37]

A. C. Rosen et al. 2003. Differential associations between entorhinal and hippocampal volumes and memory performance in older adults 117, 6 (2003), 1150.

[38]

C. R. Jack et al. 2000. Rates of hippocampal atrophy correlate with change in clinical status in aging and AD 55, 4 (2000), 484–490.

[39]

A. Qiu and M. I. J. N. Miller. 2008. Multi-structure network shape analysis via normal surface momentum maps 42, 4 (2008), 1430–1438.

[40]

M. A. Yassa, S. M. Stark, A. Bakker, M. S. Albert, M. Gallagher, and C. E. J. N. Stark. 2010. High-resolution structural and functional MRI of hippocampal CA3 and dentate gyrus in patients with amnestic mild cognitive impairment 51, 3 (2010), 1242–1252.

[41]

H. E. Scharfman and M. V. Chao. 2013. The entorhinal cortex and neurotrophin signaling in Alzheimer's disease and other disorders. Cognitive Neuroscience 4, 3-4 (2013), 123–135.

[42]

R. Killiany et al. 2002. MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology 58, 8 (2002), 1188–1196.

[43]

R. I. Scahill, J. M. Schott, J. M. Stevens, M. N. Rossor, and N. C. J. P. O. T. N. A. O. S. Fox. 2002. Mapping the evolution of regional atrophy in Alzheimer's disease: Unbiased analysis of fluid-registered serial MRI 99, 7 (2002), 4703–4707.

[44]

R. S. Desikan et al. 2012. Amyloid-β–associated clinical decline occurs only in the presence of elevated p-tau 69, 6 (2012), 709–713.

[45]

S. F. Eskildsen et al. 2013. Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning 65 (2013), 511–521.

[46]

M. Ewers et al. 2012. Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance 33, 7 (2012), 1203–1214. e2.

[47]

N. Falgàs et al. 2019. Hippocampal atrophy has limited usefulness as a diagnostic biomarker on the early onset Alzheimer's disease patients: A comparison between visual and quantitative assessment 23 (2019), 101927.

[48]

S. J. Teipel, E. Cavedo, H. Hampel, M. J. Grothe, A. S. D. N. Initiative, and A. P. M. I. J. F. I. Neurology. 2018. Basal forebrain volume, but not hippocampal volume, is a predictor of global cognitive decline in patients with Alzheimer's disease treated with cholinesterase inhibitors 9 (2018), 642.

[49]

B. Thomas, R. Sheelakumari, S. Kannath, S. Sarma, and R. J. A. J. O. N. Menon. 2019. Regional cerebral blood flow in the posterior cingulate and precuneus and the entorhinal cortical atrophy score differentiate mild cognitive impairment and dementia due to Alzheimer disease 40, 10 (2019), 1658–1664.

[50]

S. X. Duggirala, S. Saharan, P. Raghunathan, P. K. J. B. Mandal. 2016. Stimulus-dependent modulation of working memory for identity monitoring: A functional MRI study 102 (2016), 55–64.

[51]

N. Khanna, W. Altmeyer, J. Zhuo, and A. Steven. 2015. Functional neuroimaging: Fundamental principles and clinical applications. The Neuroradiology Journal 28, 2 (2015), 87–96.

[52]

R. B. J. R. O. P. I. P. Buxton. 2013. The physics of functional magnetic resonance imaging (fMRI) 76, 9 (2013), 096601.

[53]

S. J. Holdsworth and R. Bammer. 2008. Magnetic resonance imaging techniques: fMRI, DWI, and PWI. In Seminars in Neurology 28, 4 (2008), 395: NIH Public Access.

[54]

B. Dickerson et al. 2005. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD 65, 3 (2005), 404–411.

[55]

K. A. Celone et al. 2006. Alterations in memory networks in mild cognitive impairment and Alzheimer's disease: An independent component analysis 26, 40 (2006), 10222–10231.

[56]

R. J. A. O. T. N. Y. A. O. S. Sperling. 2007. Functional MRI studies of associative encoding in normal aging, mild cognitive impairment, and Alzheimer's disease 1097, 1 (2007), 146–155.

[57]

M. J. Müller et al. 2005. Functional implications of hippocampal volume and diffusivity in mild cognitive impairment 28, 4 (2005), 1033–1042.

[58]

K. Kantarci et al. 2005. DWI predicts future progression to Alzheimer disease in amnestic mild cognitive impairment 64, 5 (2005), 902–904.

[59]

H.-G. Kim et al. 2017. Quantitative susceptibility mapping to evaluate the early stage of Alzheimer's disease 16 (2017), 429–438.

[60]

J. Acosta-Cabronero, G. B. Williams, A. Cardenas-Blanco, R. J. Arnold, V. Lupson, and P. J. J. P. O. Nestor. 2013. In vivo quantitative susceptibility mapping (QSM) in Alzheimer's disease 8, 11 (2013).

[61]

N. Yan and J. Zhang. 2020. Iron metabolism, ferroptosis, and the links with Alzheimer's disease. Frontiers in Neuroscience 13, (2020),1443.

[62]

L. Du et al. 2018. Increased iron deposition on brain quantitative susceptibility mapping correlates with decreased cognitive function in Alzheimer's disease 9, 7 (2018), 1849–1857.

[63]

W. Klunk, K. Panchalingam, J. Moossy, R. McClure, and J. J. N. Pettegrew. 1992. N-acetyl-L-aspartate and other amino acid metabolites in Alzheimer's disease brain: a preliminary proton nuclear magnetic resonance study 42, 8 (1992), 1578–1578.

[64]

R. A. Moats, T. Ernst, T. K. Shonk, and B. D. J. M. R. I. M. Ross. 1994. Abnormal cerebral metabolite concentrations in patients with probable Alzheimer disease 32, 1 (1994), 110–115.

[65]

P. Mohanakrishnan et al. 1997. Regional metabolic alterations in Alzheimer's disease: An in vitro 1H NMR study of the hippocampus and cerebellum 52, 2 (1997), B111–B117.

[66]

N. Schuff et al. 2002. Selective reduction of N-acetylaspartate in medial temporal and parietal lobes in AD 58, 6 (2002), 928–935.

[67]

K. Kantarci et al. 2000. Regional metabolic patterns in mild cognitive impairment and Alzheimer's disease: A 1H MRS study 55, 2 (2000), 210–217.

[68]

D. Meyrhoff et al. 1994. Axonal injury and membrane alterations in Alzheimer's disease suggested by in vivo proton magnetic resonance spectroscopic imaging 36, 1 (1994), 40–47.

[69]

A. Pfefferbaum, E. Adalsteinsson, D. Spielman, E. V. Sullivan, and K. O. J. M. R. I. M. A. O. J. O. T. I. S. f. M. R. I. M. Lim. 1999. In vivo spectroscopic quantification of the N-acetyl moiety, creatine, and choline from large volumes of brain gray and white matter: Effects of normal aging 41, 2 (1999), 276–284.

[70]

S. Rose et al. 1999. A 1H MRS study of probable Alzheimer's disease and normal aging: Implications for longitudinal monitoring of dementia progression 17, 2 (1999), 291–299.

[71]

N. Schuff et al. 1997. Changes of hippocampal N-acetyl aspartate and volume in Alzheimer's disease: A proton MR spectroscopic imaging and MRI study 49, 6 (1997), 1513–1521.

[72]

M. Swamynathan. 2019. Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python. Apress, 2019.

[73]

R. Boutaba et al. 2018. A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities. Journal of Internet Services and Applications 9, 1 (2018), 1–99.

[74]

Y. LeCun, Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521, 2015.

[75]

N. O'Mahony et al. 2019. Deep learning vs. traditional computer vision. In Science and Information Conference 2019, 128–144: Springer.

[76]

H. Liang, X. Sun, Y. Sun, and Y. Gao. 2017. Text feature extraction based on deep learning: A review. EURASIP Journal on Wireless Communications and Networking 1 (2017), 1–12.

[77]

K. Gopalakrishnan, S. K. Khaitan, A. Choudhary, and A. Agrawal. 2017. Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials 157 (2017), 322–330.

[78]

R. Mehra. 2018. Breast cancer histology images classification: Training from scratch or transfer learning? ICT Express 4, 4 (2018), 247–254.

[79]

S. Sharma and R. Mehra. 2019. Effect of layer-wise fine-tuning in magnification-dependent classification of breast cancer histopathological image. The Visual Computer 1–15, 2019.

[80]

S. Sharma and R. Mehra. 2020. Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images—a comparative insight. Journal of Digital Imaging 1–23, 2020.

[81]

F. Saeed, T. Eslami, V. Mirjalili, A. Fong, and A. Laird. 2019. ASD-DiagNet: A hybrid learning approach for detection of autism spectrum disorder using fMRI data. Frontiers in Neuroinformatics 13, 70 2019.

[82]

S. Rathore, M. Habes, M. A. Iftikhar, A. Shacklett, and C. J. N. Davatzikos. 2017. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages 155, (2017), 530–548.

[83]

M. Tanveer et al. 2020. Machine learning techniques for the diagnosis of Alzheimer's disease: A review. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 1s (2020), 1–35.

Digital Library

[84]

E.-S. A. El-Dahshan, T. Hosny, and A.-B. M. J. D. S. P. Salem. 2010. Hybrid intelligent techniques for MRI brain images classification 20, 2 (2010), 433–441.

[85]

S.-H. Wang et al. 2018. Single slice based detection for Alzheimer's disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization 77, 9 (2018), 10393–10417.

[86]

Y. Zhang et al. 2018. Multivariate approach for Alzheimer's disease detection using stationary wavelet entropy and predator-prey particle swarm optimization 65, 3 (2018), 855–869.

[87]

Y. Zhang and S. J. P. Wang. 2015. Detection of Alzheimer's disease by displacement field and machine learning 3, e1251, 2015.

[88]

Y. Zhang, S. Wang, P. Sun, P. J. B.-M. M. Phillips.2015. Pathological brain detection based on wavelet entropy and Hu moment invariants 26, s1 (2015), S1283–S1290.

[89]

K. Hett, V.-T. Ta, J. V. Manjón, P. Coupé, A. S. D. N. I. J. C. M. Imaging and Graphics. 2018. Adaptive fusion of texture-based grading for Alzheimer's disease classification 70 (2018), 8–16.

[90]

W. Ayadi, W. Elhamzi, I. Charfi, M. J. B. S. P. Atri, and Control. 2019. A hybrid feature extraction approach for brain MRI classification based on Bag-of-words 48 (2019), 144–152.

[91]

U. R. Acharya et al. 2019. Automated detection of Alzheimer's disease using brain MRI images–a study with various feature extraction techniques 43, 9 (2019), 302.

[92]

E. J. Hwang et al. 2016. Texture analyses of quantitative susceptibility maps to differentiate Alzheimer's disease from cognitive normal and mild cognitive impairment. Medical Physics 43, 8Part1, 4718–4728, 2016.

[93]

S. Wang, Y. Zhang, G. Liu, P. Phillips, and T.-F. J. J. O. A. S. D. Yuan. 2016. Detection of Alzheimer's disease by three-dimensional displacement field estimation in structural magnetic resonance imaging 50, 1 (2016), 233–248.

[94]

S.-H. Wang et al. 2017. Alzheimer's disease detection by pseudo Zernike moment and linear regression classification 16, 1 (2017), 11–15.

[95]

H. Gorji and J. J. N. Haddadnia. 2015. A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI 305 (2015), 361–371.

[96]

K. Oh, Y.-C. Chung, K. W. Kim, W.-S. Kim, and I.-S. J. S. R. Oh. 2019. Classification and visualization of Alzheimer's disease using volumetric convolutional neural network and transfer learning 9, 1 (2019), 1–16.

[97]

F. Falahati, E. Westman, and A. Simmons. 2014. Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging. Journal of Alzheimer's Disease 41, 3 (2014), 685–708.

[98]

A. Khan and M. Usman. 2015. Early diagnosis of Alzheimer's disease using machine learning techniques: A review paper. In 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) 1 (2015), 380–387: IEEE.

Digital Library

[99]

M. R. Arbabshirani, S. Plis, J. Sui, and V. D. Calhoun. 2017. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 145, 137–165 2017.

[100]

M. Lohar and R. Patange. A Survey on Classification Methods of Brain MRI for Alzheimer's Disease.

[101]

M. A. Ebrahimighahnavieh, S. Luo, and R. Chiong. 2020. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. Computer Methods and Programs in Biomedicine 187 (2020), 105242.

Digital Library

[102]

X. Zheng, J. Shi, Y. Li, X. Liu, and Q. Zhang. 2016. Multi-modality stacked deep polynomial network based feature learning for Alzheimer's disease diagnosis. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) 2016, 851–854: IEEE.

[103]

H.-I. Suk, S.-W. Lee, and D. Shen. 2015. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Structure and Function 220, 2 (2015), 841–859.

[104]

S. Liu et al. 2014. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Transactions on Biomedical Engineering 62, 4 (2014), 1132–1140.

[105]

H.-I. Suk, S.-W. Lee, D. Shen, and A. S. D. N. Initiative. 2014. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101, (2014) 569–582.

[106]

T. Zhou, K. H. Thung, X. Zhu, and D. Shen. 2019. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Human Brain Mapping 40, 3 (2019), 1001–1016.

[107]

G. Abdi, F. Samadzadegan, and P. Reinartz. 2018. Deep learning decision fusion for the classification of urban remote sensing data. Journal of Applied Remote Sensing 12, 1 (2018), 016038.

[108]

Z.-Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu. 2019. Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems 30, 11 (2019), 3212–3232.

[109]

S. Alelyani. 2013. On Feature Selection Stability: A Data Perspective. CiteSeer 2013.

[110]

I. Guyon and A. Elisseeff. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3, (2003), 1157–1182.

Digital Library

[111]

A. Bommert, X. Sun, B. Bischl, J. Rahnenführer, and M. Lang. 2020. Benchmark for filter methods for feature selection in high-dimensional classification data. Computational Statistics & Data Analysis 143. (2020), 106839.

Digital Library

[112]

G. Brown, A. Pocock, M.-J. Zhao, and M. Luján. 2012. Conditional likelihood maximisation: A unifying framework for information theoretic feature selection. The Journal of Machine Learning Research 13, 1 (2012), 27–66.

Digital Library

[113]

Q. Gu, Z. Li, and J. Han. 2012. Generalized Fisher score for feature selection. arXiv preprint arXiv:1202.3725, 2012.

[114]

N. A. Ahad and S. S. S. Yahaya. 2014. Sensitivity analysis of Welch's t-test. In AIP Conference Proceedings 2014, 1605, 1 (2014), 888–893: American Institute of Physics.

[115]

J. Van Hulse, T. M. Khoshgoftaar, A. Napolitano, and R. Wald. 2012. Threshold-based feature selection techniques for high-dimensional bioinformatics data. Network Modeling Analysis in Health Informatics and Bioinformatics 1, 1-2 (2012), 47–61.

[116]

C. Parmar, P. Grossmann, D. Rietveld, M. M. Rietbergen, P. Lambin, and H. J. Aerts. 2015. Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Frontiers in Oncology 5, (2015), 272.

[117]

H. Liu, M. Zhou, X. S. Lu, and C. Yao. 2018. Weighted Gini index feature selection method for imbalanced data. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC) 2018, 1–6: IEEE.

[118]

I.-H. Lee, G. H. Lushington, and M. Visvanathan. 2011. A filter-based feature selection approach for identifying potential biomarkers for lung cancer. Journal of Clinical Bioinformatics 1, 1 (2011), 11.

[119]

H. Peng, F. Long, and C. Ding. 2005. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 8 (2005), 1226–1238.

Digital Library

[120]

D. Lin and X. Tang. 2006. Conditional infomax learning: An integrated framework for feature extraction and fusion. In European Conference on Computer Vision 2006, 68–82: Springer.

Digital Library

[121]

P. E. Meyer and G. Bontempi. 2006. On the use of variable complementarity for feature selection in cancer classification. In Workshops on Applications of Evolutionary Computation 2006, 91–102: Springer.

Digital Library

[122]

F. Fleuret. 2004. Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research 5, (2004), 1531–1555.

Digital Library

[123]

V.-S. Ha and H.-N. Nguyen. 2016. FRFE: Fast recursive feature elimination for credit scoring. In International Conference on Nature of Computation and Communication 2016, 133–142: Springer.

[124]

Y. Mao and Y. Yang. 2019. A wrapper feature subset selection method based on randomized search and multilayer structure. BioMed Research International 2019.

[125]

M. Zhou. 2016. Hybrid feature selection method based on Fisher score and genetic algorithm. Journal of Mathematical Sciences: Advances and Applications 37 (2016), 51–78.

[126]

Q. Chen, Z. Meng, and R. Su. 2020. WERFE: A gene selection algorithm based on recursive feature elimination and ensemble strategy. Frontiers in Bioengineering and Biotechnology 8, 2020.

[127]

Y. Zhang, Q. Deng, W. Liang, and X. Zou. 2018. An efficient feature selection strategy based on multiple support vector machine technology with gene expression data. BioMed Research International 2018.

[128]

J. Pirgazi, M. Alimoradi, T. E. Abharian, and M. H. Olyaee. 2019. An efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets. Scientific Reports 9, 1 (2019), 1—15s.

[129]

R. Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58, 1 (1996), 267–288.

[130]

K. Knight and W. Fu. 2000. Asymptotics for lasso-type estimators. Annals of Statistics 1356–1378.

[131]

J. Huang, S. Ma, and C.-H. Zhang. 2008. Adaptive lasso for sparse high-dimensional regression models. Statistica Sinica 1603–1618.

[132]

L. Jacob, G. Obozinski, and J.-P. Vert. 2009. Group lasso with overlap and graph lasso. In Proceedings of the 26th Annual International Conference on Machine Learning 2009, 433–440.

Digital Library

[133]

X. Liu, P. Cao, A. R. Gonçalves, D. Zhao, and A. Banerjee. 2018. Modeling Alzheimer's disease progression with fused Laplacian sparse group lasso. ACM Transactions on Knowledge Discovery from Data (TKDD) 12, 6 (2018), 1–35.

Digital Library

[134]

Y.-D. Zhang, S. Wang, and Z. Dong. 2014. Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Progress In Electromagnetics Research 144 (2014), 171–184.

[135]

A. Savio and M. Graña. 2013. Deformation based feature selection for computer aided diagnosis of Alzheimer's disease. 2013. Expert Systems with Applications 40, 5 (2013), 1619–1628.

Digital Library

[136]

C. Plant et al. 2010. Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease. Neuroimage 50, 1 (2010), 162–174.

[137]

Y. Zhang et al. 2015. Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning. Frontiers in Computational Neuroscience 9, 66 (2015).

[138]

Y. Zhang and S. Wang. 2015. Detection of Alzheimer's disease by displacement field and machine learning. PeerJ 3, e1251, 2015.

[139]

M. Velazquez, Y. Lee, and A. S. D. N. Initiative. 2021. Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects. Plos One 16, 4 (2021), e0244773.

[140]

T.-T. Nguyen, J. Z. Huang, and T. T. Nguyen. 2015. Unbiased feature selection in learning random forests for high-dimensional data. The Scientific World Journal 2015.

[141]

Y. Wang and X. S. Ni. 2019. A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization. arXiv preprint arXiv:1901.08433, 2019.

[142]

M. Chen, Q. Liu, S. Chen, Y. Liu, C.-H. Zhang, and R. Liu. 2019. XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system. IEEE Access 7 (2019), 13149–13158.

[143]

L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin. 2018. CatBoost: Unbiased boosting with categorical features. In Advances in Neural Information Processing Systems 2018, 6638–6648.

[144]

H. Bittencourt and R. Clarke. 2004. Feature selection by using classification and regression trees (CART). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2004.

[145]

D. Shukla et al. 2018. A multi-center study on human brain glutathione conformation using magnetic resonance spectroscopy. Journal of Alzheimer's Disease 66, 2 (2018), 517–532.

[146]

J. Grus. 2015. Data science from scratch: First principles with Python. O'Reilly Media ed: Inc, 2015.

[147]

S. Ioffe and C. Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.

[148]

P. Juszczak, D. Tax, and R. P. Duin. 2002. Feature scaling in support vector data description. In Proc. Asci 2002, 95–102: CiteSeer.

[149]

S. Raschka. 2014. About feature scaling and normalization and the effect of standardization for machine learning algorithms. Polar Political Legal Anthropology Rev 30, 1 (2014), 67–89.

[150]

K. A. Linn, B. Gaonkar, T. D. Satterthwaite, J. Doshi, C. Davatzikos, and R. T. Shinohara. 2016. Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine. NeuroImage 132 (2016), 157–166.

[151]

Y. Gu, K. Yang, S. Fu, S. Chen, X. Li, and I. Marsic. 2018. Hybrid attention based multimodal network for spoken language classification. In Proceedings of the Conference Association for Computational Linguistics. Meeting 2018, 2379: NIH Public Access.

[152]

D. Lahat, T. Adalý, and C. Jutten. 2014. Challenges in multimodal data fusion. In 2014 22nd European Signal Processing Conference (EUSIPCO) 2014, 101–s105: IEEE.

[153]

S. Planet and I. Iriondo. 2012. Comparison between decision-level and feature-level fusion of acoustic and linguistic features for spontaneous emotion recognition. In 7th Iberian Conference on Information Systems and Technologies (CISTI 2012), 2012, 1–6: IEEE.

[154]

U. Gawande, M. Zaveri, A. J. A. C. I. Kapur, and S. Computing. 2013. A novel algorithm for feature level fusion using SVM classifier for multibiometrics-based person identification 2013.

[155]

A. A. Ross and R. Govindarajan. 2005. Feature level fusion of hand and face biometrics. In Biometric Technology for Human Identification II, 2005 5779, 196–204: International Society for Optics and Photonics.

[156]

G. K. Verma and U. S. Tiwary. 2014. Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals. NeuroImage 102 (2014), 162–172.

[157]

D. Zhang, F. Song, Y. Xu, and Z. Liang. 2009. Decision level fusion. In Advanced Pattern Recognition Technologies with Applications to Biometrics: IGI Global, 2009, 328–348.

[158]

K. Veeramachaneni, L. Osadciw, A. Ross, and N. Srinivas. 2008. Decision-level fusion strategies for correlated biometric classifiers. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, 1–6: IEEE.

[159]

S.-I. Oh and H.-B. Kang. 2017. Object detection and classification by decision-level fusion for intelligent vehicle systems. Sensors 17, 1 (2017), 207.

[160]

Y. Peng, X. Zhou, D. Z. Wang, I. Patwa, D. Gong, and C. Fang. 2016. Multimodal ensemble fusion for disambiguation and retrieval. IEEE MultiMedia, 2016.

[161]

J. Wang, M. S. Kankanhalli, W. Yan, and R. Jain. 2003. Experiential sampling for video surveillance. In First ACM SIGMM International Workshop on Video Surveillance 2003, 77–86.

Digital Library

[162]

H. J. Nock, G. Iyengar, and C. Neti. 2002. Assessing face and speech consistency for monologue detection in video. In Proceedings of the Tenth ACM International Conference on Multimedia (2002), 303–306.

Digital Library

[163]

A. Jain, K. Nandakumar, and A. Ross. 2005. Score normalization in multimodal biometric systems. Pattern Recognition 38, 12 (2005), 2270–2285.

Digital Library

[164]

R. R. Brooks and S. S. Iyengar. 1998. Multi-sensor Fusion: Fundamentals and Applications with Software. Prentice-Hall, Inc., 1998.

[165]

R. C. King, E. Villeneuve, R. J. White, R. S. Sherratt, W. Holderbaum, and W. S. Harwin. 2017. Application of data fusion techniques and technologies for wearable health monitoring. Medical Engineering & Physics 42 (2017), 1–12.

[166]

X. Zou and B. Bhanu. 2005. Tracking humans using multi-modal fusion. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)-Workshops 2005, 4–4: IEEE.

[167]

R. Cutler and L. Davis. 2000. Look who's talking: Speaker detection using video and audio correlation. In 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No. 00TH8532) 3 (2000), 1589–1592: IEEE.

[168]

J. Ni, X. Ma, L. Xu, and J. Wang. 2004. An image recognition method based on multiple bp neural networks fusion. In International Conference on Information Acquisition, 2004. Proceedings, 2004, 323–326: IEEE.

[169]

F. Castanedo. 2013. A review of data fusion techniques. The Scientific World Journal 2013.

[170]

G. Papandreou, A. Katsamanis, V. Pitsikalis, P. J. I. T. O. A. Maragos, Speech and L. Processing. 2009. Adaptive multimodal fusion by uncertainty compensation with application to audiovisual speech recognition 17, 3 (2009), 423–435.

[171]

R. C. Luo, C.-C. Yih, and K. L. J. I. S. J. Su. 2002. Multisensor fusion and integration: Approaches, applications, and future research directions 2, 2 (2002), 107–119.

[172]

B. S. Reddy. 2007. Evidential Reasoning for Multimodal Fusion in Human Computer Interaction. University of Waterloo 2007.

[173]

Y. Gupta et al. 2019. Early diagnosis of Alzheimer's disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images 14, 10 (2019), e0222446.

[174]

C. R. Munteanu et al. 2015. Classification of mild cognitive impairment and Alzheimer's disease with machine-learning techniques using 1H magnetic resonance spectroscopy data. Expert Systems with Applications 42, 15–16 6205–6214.

Digital Library

[175]

R. Perneczky, S. Wagenpfeil, K. Komossa, T. Grimmer, J. Diehl, and A. Kurz. 2006. Mapping scores onto stages: Mini-mental state examination and clinical dementia rating. The American Journal of Geriatric Psychiatry 14, 2 (2006), 139–144.

[176]

N. J. Tustison et al. 2010. N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging 29, 6 (2010), 1310–1320.

[177]

Y. Xu, S. Hu, and Y. Du. 2019. Bias correction of multiple MRI images based on an improved nonparametric maximum likelihood method. IEEE Access 7 (2019), 166762–166775.

[178]

K. Friston. 2014. SPM12 Toolbox, 2014.

[179]

L. Fan et al. 2016. The human Brainnetome atlas: A new brain atlas based on connectional architecture. Cerebral Cortex 26, 8 (2016), 3508–3526.

[180]

P. K. Mandal and D. J. J. O. A. S. D. Shukla. 2018. Brain metabolic, structural, and behavioral pattern learning for early predictive diagnosis of Alzheimer's disease 63, 3 (2018), 935–939.

[181]

H. Prasad and R. Rao. 2018. Amyloid clearance defect in ApoE4 astrocytes is reversed by epigenetic correction of endosomal pH. Proceedings of the National Academy of Sciences 115, 28 (2018), E6640–E6649.

[182]

H. He and E. A. Garcia. 2009. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21, 9 (2009), 1263–1284.

Digital Library

[183]

E. Rendón, R. Alejo, C. Castorena, F. J. Isidro-Ortega, and E. E. Granda-Gutiérrez. 2020. Data sampling methods to deal with the big data multi-class imbalance problem. Applied Sciences 10, 4 (2020), 1276.

[184]

C. Shorten and T. M. Khoshgoftaar. 2019. A survey on image data augmentation for deep learning. Journal of Big Data 6, 1 (2019), 60.

[185]

J. Ngiam et al. 2019. Starnet: Targeted computation for object detection in point clouds. arXiv preprint arXiv:1908.11069, 2019.

[186]

H.-C. Shin et al. 2018. Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In International Workshop on Simulation and Synthesis in Medical Imaging 2018, 1–11: Springer.

[187]

T. Papastergiou, E. I. Zacharaki, and V. Megalooikonomou. 2018. Tensor decomposition for multiple-instance classification of high-order medical data. Complexity 2018.

Digital Library

[188]

W. Zhu, X. Wang, and H. Li. 2019. Multi-modal deep analysis for multimedia. IEEE Transactions on Circuits and Systems for Video Technology 2019.

[189]

X. Chen et al. 2015. 3D object proposals for accurate object class detection. In Advances in Neural Information Processing Systems 2015, 424–432.

[190]

A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio. 2014. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550, 2014.

[191]

K. Kang et al. 2017. Object detection in videos with tubelet proposal networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017, 727–735.

[192]

J. Dong, X. Fei, and S. Soatto. 2017. Visual-inertial-semantic scene representation for 3D object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017, 960–970.

[193]

K.-H. Thung, C.-Y. Wee, P.-T. Yap, D. Shen, and A. S. D. N. Initiative. 2014. Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. NeuroImage 91, (2014), 386–400.

[194]

K.-H. Thung, P.-T. Yap, and D. Shen. 2017. Multi-stage diagnosis of Alzheimer's disease with incomplete multimodal data via multi-task deep learning. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Springer 2017, 160–s168.

[195]

C. E. Brodley and M. A. Friedl. 1999. Identifying mislabeled training data. Journal of Artificial Intelligence Research 11 (1999), 131–167.

[196]

A. Malossini, E. Blanzieri, and R. T. Ng. 2006. Detecting potential labeling errors in microarrays by data perturbation. Bioinformatics 22, 17 (2006), 2114–2121.

Digital Library

[197]

Y. Wu and Y. Liu. 2007. Robust truncated hinge loss support vector machines. Journal of the American Statistical Association 102, 479 (2007), 974–983.

[198]

S. Y. Park and Y. Liu. 2011. Robust penalized logistic regression with truncated loss functions. Canadian Journal of Statistics 39, 2 (2011), 300–323.

[199]

J. Bootkrajang and A. Kabán. 2012. Label-noise robust logistic regression and its applications. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases 2012, 143–158: Springer.

[200]

S. Lee, H. Shin, and S. H. Lee. 2016. Label-noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study. Biometrics 72, 4 (2016), 1325–1335.

Cited By

Zhang CFan WLi HChen C(2024)Multi-level graph regularized robust multi-modal feature selection for Alzheimer’s disease classificationKnowledge-Based Systems10.1016/j.knosys.2024.111676293(111676)Online publication date: Jun-2024
https://doi.org/10.1016/j.knosys.2024.111676
Shaik TTao XLi LXie HVelásquez J(2024)A survey of multimodal information fusion for smart healthcareInformation Fusion10.1016/j.inffus.2023.102040102:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.inffus.2023.102040
Odusami MDamaševičius RMilieškaitė-Belousovienė EMaskeliūnas R(2024)Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimizationHeliyon10.1016/j.heliyon.2024.e3440210:15(e34402)Online publication date: Aug-2024
https://doi.org/10.1016/j.heliyon.2024.e34402
Show More Cited By

Index Terms

A Comprehensive Report on Machine Learning-based Early Detection of Alzheimer's Disease using Multi-modal Neuroimaging Data
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging

Recommendations

Multiple-ensemble methods for prediction of Alzheimer disease

Alzheimer's Disease (AD) is a neurodegenerative disease whose permanent cure is not yet available. However, its prediction at an early stage may increase the life span of a person by many years. The main predicament is to detect AD at an early stage and ...
Ensemble learning-based early detection of influenza disease
Abstract
Across the world, the seasonal disease influenza is a respiratory illness that impacts all age groups in many ways. Its symptoms are fever, chills, aches, pains, headaches, fatigue, cough, and weakness. Seasonal influenza can cause mild to severe ...
Early Diagnosis of Alzheimer's Disease using Machine Learning Based Methods
IC3-2021: Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing

Alzheimer's Disease is a gradual, irreversible brain disease that deteriorates a patient's memory, cognitive functions and shrinks the brain's size, eventually leading to death. Based on recent research, it is found that AD is the third leading cause of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 2

February 2023

803 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3505209

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2022

Accepted: 01 September 2021

Revised: 01 July 2021

Received: 01 October 2020

Published in CSUR Volume 55, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Refereed

Funding Sources

India-Australia Strategic Biotechnology Funding
Ministry of Information Technology

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

26
Total Citations
View Citations
2,834
Total Downloads

Downloads (Last 12 months)696
Downloads (Last 6 weeks)59

Reflects downloads up to 11 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang CFan WLi HChen C(2024)Multi-level graph regularized robust multi-modal feature selection for Alzheimer’s disease classificationKnowledge-Based Systems10.1016/j.knosys.2024.111676293(111676)Online publication date: Jun-2024
https://doi.org/10.1016/j.knosys.2024.111676
Shaik TTao XLi LXie HVelásquez J(2024)A survey of multimodal information fusion for smart healthcareInformation Fusion10.1016/j.inffus.2023.102040102:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.inffus.2023.102040
Odusami MDamaševičius RMilieškaitė-Belousovienė EMaskeliūnas R(2024)Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimizationHeliyon10.1016/j.heliyon.2024.e3440210:15(e34402)Online publication date: Aug-2024
https://doi.org/10.1016/j.heliyon.2024.e34402
Bian GZheng JLi ZWang JFu PXin CSilva DWu WAlbuquerque V(2024)BiMNetExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121885238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121885
Almatrafi SAbbas QIbrahim M(2024)A systematic literature review of machine learning approaches for class-wise recognition of Alzheimer’s disease using neuroimaging-based brain disorder analysisMultimedia Tools and Applications10.1007/s11042-024-19104-zOnline publication date: 24-Apr-2024
https://doi.org/10.1007/s11042-024-19104-z
Sajid MSharma RBeheshti ITanveer M(2024)Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concernWIREs Data Mining and Knowledge Discovery10.1002/widm.154614:5Online publication date: 28-May-2024
https://doi.org/10.1002/widm.1546
Odusami MDamasevicius RMilieskaite‐Belousoviene EMaskeliunas R(2024)Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision TransformerInternational Journal of Imaging Systems and Technology10.1002/ima.2315834:5Online publication date: 26-Aug-2024
https://doi.org/10.1002/ima.23158
Lee E(2023)Memory Deficits in Parkinson’s Disease Are Associated with Impaired Attentional Filtering and Memory Consolidation ProcessesJournal of Clinical Medicine10.3390/jcm1214459412:14(4594)Online publication date: 10-Jul-2023
https://doi.org/10.3390/jcm12144594
Khalid ASenan EAl-Wagih KAl-Azzam MAlkhraisha Z(2023)Automatic Analysis of MRI Images for Early Prediction of Alzheimer’s Disease Stages Based on Hybrid Features of CNN and Handcrafted FeaturesDiagnostics10.3390/diagnostics1309165413:9(1654)Online publication date: 8-May-2023
https://doi.org/10.3390/diagnostics13091654
Castro FImpedovo DPirlo G(2023)A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated TransmissionApplied Sciences10.3390/app1310609913:10(6099)Online publication date: 16-May-2023
https://doi.org/10.3390/app13106099
Show More Cited By

View Options

Get Access

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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents