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Classification of Alzheimer’s disease based on brain MRI and machine learning

  • Deep Learning & Neural Computing for Intelligent Sensing and Control
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Abstract

Alzheimer’s disease (AD) is one of the most common diseases in the world. It is a neurodegenerative disease that can cause cognitive impairment and memory deterioration. In recent years, the number of the elderly population is increasing, and the incidence of elderly diseases has increased significantly. The most representative of these diseases is Alzheimer’s disease. According to some data, the average survival time of Alzheimer’s disease patients is only 5.5 years, which is the “fourth killer” that endangers the health of the elderly after cardiovascular diseases, cerebrovascular diseases and cancer. According to conservative estimates of the International Federation of Alzheimer’s Diseases, the number of Alzheimer’s disease patients worldwide will increase to 75.62 million by 2030; by 2050, the number of patients will reach 135.46 million. Therefore, it is urgent to classify the course of Alzheimer’s disease. In this paper, support vector machine (SVM) model method is used to classify and predict different disease processes of Alzheimer’s disease based on structural brain magnetic resonance imaging (MRI) imaging data, so as to help the auxiliary diagnosis of the disease. In this paper, the extracted MRI data and the SVM model are combined to obtain more accurate classification prediction results. The accuracy of classification and prediction is the best. According to the predicted results, the data characteristics related to diseases can be determined, which can provide a basis for clinical and basic research, etiology and pathological changes.

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References

  1. Reitz C, Mayeux R (2014) Alzheimer disease: epidemiology, diagnostic criteria, risk factors and biomarkers. Biochem Pharmacol 88(4):640–651

    Article  Google Scholar 

  2. Green RC, Cupples LA, Kurz A et al (2016) Depression as a risk factor for Alzheimer disease: the MIRAGE study. Arch Neurol 60(5):753

    Article  Google Scholar 

  3. Bloom GS (2014) Amyloid-β and tau: the trigger and bullet in Alzheimer disease pathogenesis. JAMA Neurol 71(4):505–508

    Article  Google Scholar 

  4. Lucía CG, Leen B, Iryna B et al (2014) The mechanism of γ-Secretase dysfunction in familial Alzheimer disease. EMBO J 31(10):2261–2274

    Google Scholar 

  5. Tarasoffconway JM, Carare RO, Osorio RS et al (2016) Clearance systems in the brain—implications for Alzheimer disease. Nat Rev Neurol 12(4):248

    Article  Google Scholar 

  6. Ferrucci R, Mameli F, Guidi I et al (2016) Transcranial direct current stimulation improves recognition memory in Alzheimer disease. Neurology 71(7):493–498

    Article  Google Scholar 

  7. Ujiie M, Dickstein DL, Carlow DA et al (2015) Blood-brain barrier permeability precedes senile plaque formation in an Alzheimer disease model. Neurobiol Aging 25(6):S236–S236

    Google Scholar 

  8. Barker WW, Luis CA, Kashuba A et al (2015) Relative frequencies of Alzheimer disease, Lewy body, vascular and frontotemporal dementia, and hippocampal sclerosis in the State of Florida Brain Bank. Alzheimer Dis Assoc Disord 16(4):203–212

    Article  Google Scholar 

  9. Tariot PN, Farlow MR, Grossberg GT et al (2014) Memantine treatment in patients with moderate to severe Alzheimer disease already receiving donepezil: a randomized controlled trial. Chin J Gen Pract 291(3):317–324

    Google Scholar 

  10. Tromp D, Dufour A, Lithfous S et al (2015) Episodic memory in normal aging and Alzheimer disease: insights from imaging and behavioral studies. Ageing Res Rev 24(Pt B):232–262

    Article  Google Scholar 

  11. Butterfield DA, Domenico FD, Barone E (2014) Elevated risk of type 2 diabetes for development of Alzheimer disease: a key role for oxidative stress in brain. Biochim Biophys Acta 1842(9):1693–1706

    Article  Google Scholar 

  12. Rd DD III, Jerskey BA, Chen K et al (2014) Brain differences in infants at differential genetic risk for late-onset Alzheimer disease: a cross-sectional imaging study. JAMA Neurol 71(1):11

    Article  Google Scholar 

  13. Takeda S, Sato N, Morishita R (2014) Systemic inflammation, blood-brain barrier vulnerability and cognitive/non-cognitive symptoms in Alzheimer disease: relevance to pathogenesis and therapy. Front Aging Neurosci 6(6):171

    Google Scholar 

  14. Willette AA, Bendlin BB, Starks EJ et al (2015) Association of insulin resistance with cerebral glucose uptake in late middle-aged adults at risk for Alzheimer disease. JAMA Neurol 72(9):1013

    Article  Google Scholar 

  15. Kester MI, Goos JD, Teunissen CE et al (2014) Associations between cerebral small-vessel disease and Alzheimer disease pathology as measured by cerebrospinal fluid biomarkers. JAMA Neurol 71(7):855–862

    Article  Google Scholar 

  16. Tramutola A, Triplett JC, Domenico FD et al (2015) Alteration of mTOR signaling occurs early in the progression of Alzheimer disease (AD): analysis of brain from subjects with pre-clinical AD, amnestic mild cognitive impairment and late-stage AD. J Neurochem 133(5):739–749

    Article  Google Scholar 

  17. Wang LY, Raskind MA, Wilkinson CW et al (2018) Associations between CSF cortisol and CSF norepinephrine in cognitively normal controls and patients with amnestic MCI and AD dementia. Int J Geriatr Psychiatry 33(5):763

    Article  Google Scholar 

  18. Prestia A, Caroli A, Wade SK et al (2015) Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics. Alzheimers Dement J Alzheimers Assoc 11(10):1191–1201

    Article  Google Scholar 

  19. Peskind ER, Tsuang DW, Bonner LT et al (2015) Propranolol for disruptive behaviors in nursing home residents with probable or possible Alzheimer disease: a placebo-controlled study. Alzheimer Dis Assoc Disord 19(1):23–28

    Article  Google Scholar 

  20. Carnevale L, D’Angelosante V, Landolfi A et al (2018) Brain MRI fiber-tracking reveals white matter alterations in hypertensive patients without damage at conventional neuroimaging. Cardiovas Res 114(11):1536–1546

    Article  Google Scholar 

  21. Amlerova J, Cavanna AE, Bradac O et al (2014) Emotion recognition and social cognition in temporal lobe epilepsy and the effect of epilepsy surgery. Epilepsy Behav 36(36):86–89

    Article  Google Scholar 

  22. Liu WF, Wang X, Xia H (2014) 3D texture analysis of Corpus Caliosum based on MR images inpatients with Alzheimer’s disease and mild cognitive impairment. Appl Mech Mater 533:415–420

    Article  Google Scholar 

  23. Yu L, Xia H, Liu W (2016) Classification studies in patients with Alzheimer’s disease and normal control group based on three-dimensional texture features of hippocampus magnetic resonance images. J Biomed Eng 33(6):1090–1094

    Google Scholar 

  24. Chen Z, Chen X, Chen Z et al (2017) Alteration of gray matter texture features over the whole brain in medication-overuse headache using a 3-dimensional texture analysis. J Headache Pain 18(1):112

    Article  Google Scholar 

  25. Leandrou S, Petroudi S, Kyriacou PA et al (2018) Quantitative MRI brain studies in mild cognitive impairment and Alzheimer’s disease: a methodological review. IEEE Rev Biomed Eng 11(99):97

    Article  Google Scholar 

  26. Liu Z, Lv L, Yong W (2017) Development of face recognition system based on PCA and LBP for intelligent anti-theft doors. In: IEEE International conference on computer and communications

  27. Wang Q (2016) Design and implementation of remote facial expression recognition surveillance system based on PCA and KNN algorithms. In: International conference on intelligent information hiding and multimedia signal processing

  28. Guo-Jun MA, Zhou HD (2015) Research and implementation of intelligent terminal lightweight face recognition system. J Commun 5(6):119–134

    Google Scholar 

  29. Liang X (2019) A medical diagnostic expert system with LPSO-based artificial neural network. Investigación Clínica 60(4):894–899

    Google Scholar 

Download references

Acknowledgements

This work was supported by: (1) Studying Abroad Scholarships by Department of Resource and Social Security of Shanxi Province (Grant/Award: 619017); (2) Shanxi scholarship council of China (Grant/Award No. 2016-061); (3) International Cooperation Project, the Shanxi Science and Technology Department (Grant/Award No. 201803D421068).

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Correspondence to Zhao Fan.

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Fan, Z., Xu, F., Qi, X. et al. Classification of Alzheimer’s disease based on brain MRI and machine learning. Neural Comput & Applic 32, 1927–1936 (2020). https://doi.org/10.1007/s00521-019-04495-0

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