Abstract
Parkinson’s Disease (PD) is one of the most common types of neurological diseases caused by progressive degeneration of dopaminergic neurons in the brain. Even though there is no fixed cure for this neurodegenerative disease, earlier diagnosis followed by earlier treatment can help patients have a better quality of life. Magnetic Resonance Imaging (MRI) has been one of the most popular diagnostic tool in recent years because it avoids harmful radiations. In this paper, we investigate the plausibility of using MRIs for automatically diagnosing PD. Our proposed method has three main steps: (1) Preprocessing, (2) Feature Extraction, and (3) Classification. The FreeSurfer library is used for the first and the second steps. For classification, three main types of classifiers, including Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM), are applied and their classification ability is compared. The Parkinson’s Progression Markers Initiative (PPMI) data set is used to evaluate the proposed method. The proposed system prove to be promising in assisting the diagnosis of PD.
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References
https://parkinsonsnewstoday.com/parkinsons-disease-statistics/
Fox, S.H., et al.: The movement disorder society evidence-based medicine review update: treatments for the motor symptoms of Parkinson’s disease. Mov. Disord. 26(S3), S2–S41 (2011)
Chaudhuri, K.R., Schapira, A.H.: Non-motor symptoms of Parkinson’s disease: dopaminergic pathophysiology and treatment. Lancet Neurol. 8(5), 464–474 (2009)
Rana, B., et al.: Relevant 3D local binary pattern based features from fused feature descriptor for differential diagnosis of Parkinson’s disease using structural MRI. Biomed. Signal Process. Control 34, 134–143 (2017)
Adeli, E., et al.: Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data. NeuroImage 141, 206–219 (2016)
Prashanth, R., Roy, S.D., Mandal, P.K., Ghosh, S.: Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging. Expert Syst. Appl. 41, 3333–3342 (2014)
Politis, M., Loane, C.: Serotonergic dysfunction in parkinson’s disease and its relevance to disability. Sci. World J. 11, 9 (2011). Article ID 172893
Focke, N.K., et al.: Individual voxel-base subtype prediction can differentiate progressive supranuclear palsy from idiopathic Parkinson syndrome and healthy controls. Hum. Brain Mapp. 32(11), 1905–1915 (2011)
Babu, G.S., Suresh, S., Mahanand, B.S.: A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease. Expert Syst. Appl. 41(2), 478–488 (2014)
Salvatore, C., et al.: Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy. J. Neurosci. Methods 222, 230–237 (2014)
Rana, B., et al.: Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI. Expert Syst. Appl. 42(9), 4506–4516 (2015)
Rana, B., et al.: Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson’s disease using T1-weighted MRI. Int. J. Imaging Syst. Technol. 25(3), 245–255 (2015)
Peng, B., et al.: A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson’s disease. Neurosci. Lett. 651, 88–94 (2017). ISSN 0304–3940
Adeli, E., et al.: Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data. NeuroImage 141, 206–219 (2016)
Worker, A., et al.: Cortical thickness, surface area and volume measures in Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy. PLoS One 9(12), e114167 (2014)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Martin, J., Jurafsky, D.: Speech and Language Processing. Prentice Hall, Upper Saddle River (2000)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995). https://doi.org/10.1007/978-1-4757-2440-0
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Soltaninejad, S., Cheng, I., Basu, A. (2018). Towards the Identification of Parkinson’s Disease Using only T1 MR Images. In: Basu, A., Berretti, S. (eds) Smart Multimedia. ICSM 2018. Lecture Notes in Computer Science(), vol 11010. Springer, Cham. https://doi.org/10.1007/978-3-030-04375-9_13
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DOI: https://doi.org/10.1007/978-3-030-04375-9_13
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