3D convolutional neural networks-based multiclass classification of Alzheimer’s and Parkinson’s diseases using PET and SPECT neuroimaging modalities
Tóm tắt
Alzheimer’s disease (AD) is a neurodegenerative brain pathology formed due to piling up of amyloid proteins, development of plaques and disappearance of neurons. Another common subtype of dementia like AD, Parkinson’s disease (PD) is determined by the disappearance of dopaminergic neurons in the region known as substantia nigra pars compacta located in the midbrain. Both AD and PD target aged population worldwide forming a major chunk of healthcare costs. Hence, there is a need for methods that help in the early diagnosis of these diseases. PD subjects especially those who have confirmed postmortem plaque are a strong candidate for a second AD diagnosis. Modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) can be combined with deep learning methods to diagnose these two diseases for the benefit of clinicians. In this work, we deployed a 3D Convolutional Neural Network (CNN) to extract features for multiclass classification of both AD and PD in the frequency and spatial domains using PET and SPECT neuroimaging modalities to differentiate between AD, PD and Normal Control (NC) classes. Discrete Cosine Transform has been deployed as a frequency domain learning method along with random weak Gaussian blurring and random zooming in/out augmentation methods in both frequency and spatial domains. To select the hyperparameters of the 3D-CNN model, we deployed both 5- and 10-fold cross-validation (CV) approaches. The best performing model was found to be AD/NC(SPECT)/PD classification with random weak Gaussian blurred augmentation in the spatial domain using fivefold CV approach while the worst performing model happens to be AD/NC(PET)/PD classification without augmentation in the frequency domain using tenfold CV approach. We also found that spatial domain methods tend to perform better than their frequency domain counterparts. The proposed model provides a good performance in discriminating AD and PD subjects due to minimal correlation between these two dementia types on the clinicopathological continuum between AD and PD subjects from a neuroimaging perspective.
Tài liệu tham khảo
Langa KM, Levine DA (2014) The diagnosis and management of mild cognitive impairment: a clinical review. JAMA 312(23):2551–2561
Tse KH, Cheng A, Ma F et al (2018) DNA damage-associated oligodendrocyte degeneration precedes amyloid pathology and contributes to Alzheimer’s disease and dementia. Alzheimers Dement 14(5):664–679
Parkinson’s disease statistics. https://parkinsonsnewstoday.com/parkinsons-disease-statistics/. Accessed 29 Sept 2021
Men more likely to get Parkinson’s disease? https://www.webmd.com/parkinsons-disease/news/20040317/men-more-likely-to-get-parkinsons-disease. Accessed 29 Sept 2021
Sajal MSR, Ehsan MT, Vaidyanathan R et al (2020) Telemonitoring Parkinson’s disease using machine learning by combining tremor and voice analysis. Brain Inf 7:12
Pontecorvo MJ, Mintun MA (2011) PET amyloid imaging as a tool for early diagnosis and identifying patients at risk for progression to Alzheimer’s disease. Alz Res Ther 3(2):11
Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
Wolz R, Aljabar P, Hajnal JV et al (2012) Nonlinear dimensionality reduction combining MR imaging with non-imaging information. Med Image Anal 16(4):819–830
Noor MBT, Zenia NZ, Kaiser MS et al (2020) Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease Parkinson’s disease and schizophrenia. Brain Inf 7:11
Mahmud M, Kaiser MS, McGinnity TM et al (2021) Deep learning in mining biological data. Cogn Comput 13:1–33
Haq AU, Li JP, Memon MH et al (2019) Feature selection based on L1-norm support vector machine and effective recognition system for Parkinson’s disease using voice recordings. IEEE Access 7:37718–37734
Arora S, Baghai-Ravary L, Tsanas A (2019) Developing a large scale population screening tool for the assessment of Parkinson’s disease using telephone-quality voice. J Acoust Soc Am 145(5):2871
Rojas A, Górriz JM, Ramírez J et al (2013) Application of empirical mode decomposition (EMD) on datscan SPECT images to explore Parkinson disease. Exp Syst Appl 40(7):2756–2766
Torfason R, Mentzer F, Agustsson E et al (2018) Towards image understanding from deep compression without decoding. In: 2018 International conference on learning representations (ICLR), Canada
Irwin DJ, White MT, Toledo JB et al (2012) Neuropathologic substrates of Parkinson disease dementia. Ann Neurol 72(4):587–598
Marek K, Jennings D, Lasch S et al (2011) The Parkinson progression marker initiative (PPMI). Prog Neurobiol 95(4):629–635
Mueller SG, Weiner MW, Thal LJ et al (2005) Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s disease neuroimaging initiative (ADNI). Alzheimers Dement 1(1):55–66
Caspell-Garcia C, Simuni T, Tosun-Turgut D et al (2017) Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease. PLoS ONE 12(5):e0175674
Ping L, Duong DM, Yin L et al (2018) Data descriptor: global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s disease. Sci Data 5:180036
Weil RS, Hsu JK, Darby RR et al (2019) Neuroimaging in Parkinson’s disease dementia: connecting the dots. Brain Commun 1(1):fcz006