{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:39:37Z","timestamp":1742960377756,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031585340"},{"type":"electronic","value":"9783031585357"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-58535-7_15","type":"book-chapter","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T17:01:50Z","timestamp":1719939710000},"page":"176-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AG-PDCnet: An Attention Guided Parkinson\u2019s Disease Classification Network with\u00a0MRI, DTI and\u00a0Clinical Assessment Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4384-8939","authenticated-orcid":false,"given":"Sushanta Kumar","family":"Sahu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5799-3467","authenticated-orcid":false,"given":"Ananda S.","family":"Chowdhury","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.future.2018.02.009","volume":"83","author":"E Abdulhay","year":"2018","unstructured":"Abdulhay, E., Arunkumar, N., Narasimhan, K., Vellaiappan, E., Venkatraman, V.: Gait and tremor investigation using machine learning techniques for the diagnosis of parkinson disease. Futur. Gener. Comput. Syst. 83, 366\u2013373 (2018)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3389\/fninf.2014.00014","volume":"8","author":"A Abraham","year":"2014","unstructured":"Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., et al.: Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014)","journal-title":"Front. Neuroinform."},{"doi-asserted-by":"crossref","unstructured":"Adeli, E., et al.: Joint feature-sample selection and robust diagnosis of parkinson\u2019s disease from MRI data. NeuroImage 141, 206\u2013219 (2016)","key":"15_CR3","DOI":"10.1016\/j.neuroimage.2016.05.054"},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.future.2018.11.054","volume":"94","author":"LC Afonso","year":"2019","unstructured":"Afonso, L.C., et al.: A recurrence plot-based approach for parkinson\u2019s disease identification. Futur. Gener. Comput. Syst. 94, 282\u2013292 (2019)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"1","key":"15_CR5","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","volume":"38","author":"J Ashburner","year":"2007","unstructured":"Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95\u2013113 (2007)","journal-title":"Neuroimage"},{"issue":"6","key":"15_CR6","doi-asserted-by":"publisher","first-page":"402","DOI":"10.3390\/diagnostics10060402","volume":"10","author":"S Chakraborty","year":"2020","unstructured":"Chakraborty, S., Aich, S., Kim, H.C.: Detection of parkinson\u2019s disease from 3T T1 weighted MRI scans using 3D convolutional neural network. Diagnostics 10(6), 402 (2020)","journal-title":"Diagnostics"},{"issue":"1","key":"15_CR7","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1186\/s12967-023-04158-8","volume":"21","author":"B Chen","year":"2023","unstructured":"Chen, B., et al.: Detection of mild cognitive impairment in parkinson\u2019s disease using gradient boosting decision tree models based on multilevel dti indices. J. Transl. Med. 21(1), 310 (2023)","journal-title":"J. Transl. Med."},{"doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","key":"15_CR8","DOI":"10.1145\/2939672.2939785"},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.compbiomed.2018.05.006","volume":"99","author":"O Cigdem","year":"2018","unstructured":"Cigdem, O., Beheshti, I., Demirel, H.: Effects of different covariates and contrasts on classification of parkinson\u2019s disease using structural MRI. Comput. Biol. Med. 99, 173\u2013181 (2018)","journal-title":"Comput. Biol. Med."},{"doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","key":"15_CR10","DOI":"10.1109\/CVPR.2019.00326"},{"doi-asserted-by":"crossref","unstructured":"Gabriel, S.L., Roberto, R.R.: Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of parkinson\u2019s disease. Comput. Methods Programs Biomed. 198 (2021)","key":"15_CR11","DOI":"10.1016\/j.cmpb.2020.105793"},{"issue":"5","key":"15_CR12","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1109\/JBHI.2018.2872811","volume":"23","author":"Z Huang","year":"2019","unstructured":"Huang, Z., Yang, C., Zhou, X., Huang, T.: A hybrid feature selection method based on binary state transition algorithm and relieff. IEEE J. Biomed. Health Inform. 23(5), 1888\u20131898 (2019)","journal-title":"IEEE J. Biomed. Health Inform."},{"doi-asserted-by":"crossref","unstructured":"Jin, D., et al.: Attention-based 3D convolutional network for alzheimer\u2019s disease diagnosis and biomarkers exploration. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1047\u20131051. IEEE (2019)","key":"15_CR13","DOI":"10.1109\/ISBI.2019.8759455"},{"doi-asserted-by":"crossref","unstructured":"Li, S., Lei, H., Zhou, F., Gardezi, J., Lei, B.: Longitudinal and multi-modal data learning for parkinson\u2019s disease diagnosis via stacked sparse auto-encoder. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 384\u2013387. IEEE (2019)","key":"15_CR14","DOI":"10.1109\/ISBI.2019.8759385"},{"unstructured":"[dataset] Marek, Jennings, D., Lasch, S., Siderowf, A., Tanner, C., et\u00a0al.: The parkinson progression marker initiative (PPMI). Prog. Neurobiol. 95, 629\u2013635 (2011)","key":"15_CR15"},{"key":"15_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102418","volume":"66","author":"L Moro-Velazquez","year":"2021","unstructured":"Moro-Velazquez, L., Gomez-Garcia, J.A., Arias-Londo\u00f1o, J.D., Dehak, N., et al.: Advances in parkinson\u2019s disease detection and assessment using voice and speech: a review of the articulatory and phonatory aspects. Biomed. Signal Process. Control 66, 102418 (2021)","journal-title":"Biomed. Signal Process. Control"},{"doi-asserted-by":"crossref","unstructured":"Park, C.H., Lee, P.H., Lee, S.K., Chung, S.J., Shin, N.Y.: The diagnostic potential of multimodal neuroimaging measures in parkinson\u2019s disease and atypical parkinsonism. Brain Behav. 10(11), e01808 (2020)","key":"15_CR17","DOI":"10.1002\/brb3.1808"},{"key":"15_CR18","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR19","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.ijmedinf.2018.09.008","volume":"119","author":"R Prashanth","year":"2018","unstructured":"Prashanth, R., Roy, S.D.: Early detection of parkinson\u2019s disease through patient questionnaire and predictive modelling. Int. J. Med. Informatics 119, 75\u201387 (2018)","journal-title":"Int. J. Med. Informatics"},{"doi-asserted-by":"crossref","unstructured":"Pristyanto, Y., Nugraha, A.F., Dahlan, A., Wirasakti, L.A., et\u00a0al.: Multiclass imbalanced handling using adasyn oversampling and stacking algorithm. In: 2022 16th International Conference on Ubiquitous Information Management and Communication, pp.\u00a01\u20135. IEEE (2022)","key":"15_CR20","DOI":"10.1109\/IMCOM53663.2022.9721632"},{"key":"15_CR21","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-981-16-1249-7_14","volume-title":"Soft Computing and Signal Processing","author":"K Rajanbabu","year":"2022","unstructured":"Rajanbabu, K., Veetil, I.K., Sowmya, V., Gopalakrishnan, E.A., Soman, K.P.: Ensemble of deep transfer learning models for parkinson\u2019s disease classification. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds.) Soft Computing and Signal Processing. AISC, vol. 1340, pp. 135\u2013143. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-1249-7_14"},{"doi-asserted-by":"crossref","unstructured":"Sahu, S.K., Chowdhury, A.: Multi-modal multi-class parkinson disease classification using CNN and decision level fusion. In: 10th International Conference on Pattern Recognition and Machine Intelligence (acepted). arXiv preprint arXiv:2307.02978 (2023)","key":"15_CR22","DOI":"10.1007\/978-3-031-45170-6_77"},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)","key":"15_CR23"},{"doi-asserted-by":"crossref","unstructured":"Singh, G., Samavedham, L., Lim, E.C.H., ADNI, PPMI, et\u00a0al.: Determination of imaging biomarkers to decipher disease trajectories and differential diagnosis of neurodegenerative diseases. J. Neurosci. Methods 305, 105\u2013116 (2018)","key":"15_CR24","DOI":"10.1016\/j.jneumeth.2018.05.009"},{"issue":"21","key":"15_CR25","doi-asserted-by":"publisher","first-page":"15467","DOI":"10.1007\/s11042-019-7469-8","volume":"79","author":"S Sivaranjini","year":"2020","unstructured":"Sivaranjini, S., Sujatha, C.: Deep learning based diagnosis of parkinson\u2019s disease using convolutional neural network. Multimedia Tools Appl. 79(21), 15467\u201315479 (2020)","journal-title":"Multimedia Tools Appl."},{"issue":"1","key":"15_CR26","doi-asserted-by":"publisher","first-page":"14036","DOI":"10.1038\/s41598-022-18015-z","volume":"12","author":"JM Templeton","year":"2022","unstructured":"Templeton, J.M., Poellabauer, C., Schneider, S.: Classification of parkinson\u2019s disease and its stages using machine learning. Sci. Rep. 12(1), 14036 (2022)","journal-title":"Sci. Rep."},{"doi-asserted-by":"crossref","unstructured":"Tremblay, C., Mei, J., Frasnelli, J.: Olfactory bulb surroundings can help to distinguish parkinson\u2019s disease from non-parkinsonian olfactory dysfunction. NeuroImage Clin. 28, 102457 (2020)","key":"15_CR27","DOI":"10.1016\/j.nicl.2020.102457"},{"key":"15_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.109987","volume":"134","author":"Y Xue","year":"2023","unstructured":"Xue, Y., Zhu, H., Neri, F.: A feature selection approach based on NSGA-II with relieff. Appl. Soft Comput. 134, 109987 (2023)","journal-title":"Appl. Soft Comput."},{"doi-asserted-by":"crossref","unstructured":"Yang, Y., Wei, L., Hu, Y., Wu, Y., Hu, L., Nie, S.: Classification of parkinson\u2019s disease based on multi-modal features and stacking ensemble learning. J. Neurosci. Methods 350 (2021)","key":"15_CR29","DOI":"10.1016\/j.jneumeth.2020.109019"},{"doi-asserted-by":"crossref","unstructured":"Zhang, G., Kan, M., Shan, S., Chen, X.: Generative adversarial network with spatial attention for face attribute editing. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 417\u2013432 (2018)","key":"15_CR30","DOI":"10.1007\/978-3-030-01231-1_26"},{"key":"15_CR31","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.mri.2021.02.001","volume":"78","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Zheng, B., Gao, A., Feng, X., Liang, D., Long, X.: A 3D densely connected convolution neural network with connection-wise attention mechanism for alzheimer\u2019s disease classification. Magn. Reson. Imaging 78, 119\u2013126 (2021)","journal-title":"Magn. Reson. Imaging"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58535-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T17:05:33Z","timestamp":1719939933000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58535-7_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031585340","9783031585357"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58535-7_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jammu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iitjammu.ac.in\/cvip2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Online CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"461","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"140","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}