Abstract
Parkinson’s disease (PD) is a long-term degenerative disorder of the central nervous system. Early diagnosis of PD has great clinical significance as patients would be able to receive specialized treatment earlier to slow down the PD progression. Many researchers proposed various machine learning methods to classify the different stages of PD using magnetic resonance imaging (MRI). However, these methods usually extract features from MRI only using a single template. In this paper, we propose a new template-oriented multi-task sparse low-rank learning (TMSLRL) method using MRI for multi-classification of PD patients. Firstly, we extract features from MRI using different templates where each template is corresponding to a particular task. These tasks form a template-oriented multi-task learning to concurrently obtain an inner relationship of each task. Secondly, sparse low-rank learning is performed to capture the potential relationships between the inputs and the outputs and select the most class-discriminative features. Finally, we feed the selected features to train the classifier to get the final classification result. Our proposed model is evaluated by the data from the Parkinson’s progression markers initiative (PPMI) dataset. Furthermore, the results of experiments we performed indicate our method have greater performance than the similar methods.
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Neurological disorders report, public health challenge (2016). https://www.who.int/mental_health/neurology/neurological_disorders_report_web.pdf
Emrani, S., McGuirk, A., Xiao, W.: Prognosis and diagnosis of Parkinson’s disease using multi-task learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1457–1466 (2017)
Dickson, D.W.: Neuropathology of Parkinson disease. Parkinsonism Relat. Disord. 46(Suppl 1), S30–S33 (2018)
Lei, B., Yang, P., Wang, T., Chen, S., Ni, D.: Relational-regularized discriminative sparse learning for Alzheimer’s disease diagnosis. IEEE Trans. Cybern. 47(4), 1102–1113 (2017)
Zhu, X., Suk, H.-I., Lee, S.-W., Shen, D.: Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607–618 (2015)
Kong, Y., Deng, Y., Dai, Q.: Discriminative clustering and feature selection for brain MRI segmentation. IEEE Signal Process. Lett. 22(5), 573–577 (2014)
Lei, B., et al.: Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis. Med. Image Anal. 61, 101632 (2020)
Lei, H., Zhao, Y., Huang, Z., Zhou, F., Huang, L., Lei, B.: Multi-classification of Parkinson’s disease via sparse low-rank learning. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3268–3272 (2018)
Chen, X., Pan, W., Kwok, J.T., Carbonell, J.G.: Accelerated gradient method for multi-task sparse learning problem. In: 2009 Ninth IEEE International Conference on Data Mining, pp. 746–751 (2009)
Jothi, G., Hannah, I.H.: Hybrid Tolerance Rough Set-Firefly based supervised feature selection for MRI brain tumor image classification. Appl. Soft Comput. 46, 639–651 (2016)
Zhang, D., Shen, D.: Alzheimer’s disease neuroimaging initiative: multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59(2), 895–907 (2012)
Zhu, X., Suk, H.-I., Shen, D.: Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification. World Wide Web 22(2), 907–925 (2018). https://doi.org/10.1007/s11280-018-0645-3
Chaudhuri, K.R., Healy, D.G., Schapira, A.H.: Non-motor symptoms of Parkinson’s disease: diagnosis and management. Lancet Neurol. 5(3), 235–245 (2006)
Fung, G., Stoeckel, J.: SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information. Knowl. Inf. Syst. 11(2), 243–258 (2007). https://doi.org/10.1007/s10115-006-0043-5
Spadoto, A.A., Guido, R.C., Carnevali, F.L., Pagnin, A.F., Falcão, A.X., Papa, J.P.: Improving Parkinson’s disease identification through evolutionary-based feature selection. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7857–7860 (2011)
Caesarendra, W., Putri, F.T., Ariyanto, M., Setiawan, J.D.: Pattern recognition methods for multi stage classification of Parkinson’s disease utilizing voice features. In: 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 802–807 (2015)
Marek, K., et al.: The Parkinson progression marker initiative (PPMI). Prog. Neurobiol. 95(4), 629–635 (2011)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999). https://doi.org/10.1023/A:1018628609742
Liaw, A., Wiener, M.: Classification and regression by randomForest. R news 2(3), 18–22 (2002)
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. SMC 15(4), 580–585 (1985)
Sadananthan, S.A., Zheng, W., Chee, M.W.L., Zagorodnov, V.: Skull stripping using graph cuts. NeuroImage 49(1), 225–239 (2010)
Penny, W.D., Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier, Amsterdam (2011)
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Chen, Z. et al. (2020). Template-Oriented Multi-task Sparse Low-Rank Learning for Parkinson’s Diseases Diagnosis. In: Rekik, I., Adeli, E., Park, S.H., Valdés Hernández, M.d.C. (eds) Predictive Intelligence in Medicine. PRIME 2020. Lecture Notes in Computer Science(), vol 12329. Springer, Cham. https://doi.org/10.1007/978-3-030-59354-4_17
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