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Deep Learning-Based Approach for Parkinson’s Disease Detection Using Region of Interest

Publication Type : Book Chapter

Publisher : Springer

Source : In Intelligent Sustainable Systems, pp. 1-13. Springer, Singapore, 2022.

Url : https://link.springer.com/chapter/10.1007/978-981-16-2422-3_1

Campus : Coimbatore

School : School of Engineering

Department : Center for Computational Engineering and Networking (CEN)

Year : 2022

Abstract : Deep Learning plays a major role in advancements in the healthcare domain, of which early disease diagnosis is one main application. With respect to the same, deep learning-based classification of brain MRI at the subject level is the requirement in the medical field. In this work, we have implemented an algorithm to identify the most discriminative range of MRI slices at the subject level to differentiate between Normal Cohorts (NC) and Parkinson’s Disease (PD) subjects. We have also focused on handling data leakage and verified the model generalizability using Stratified k-fold cross-validation.

Cite this Research Publication : Madan, Yamini, Iswarya Kannoth Veetil, V. Sowmya, E. A. Gopalakrishnan, and K. P. Soman. "Deep Learning-Based Approach for Parkinson’s Disease Detection Using Region of Interest." In Intelligent Sustainable Systems, pp. 1-13. Springer, Singapore, 2022.

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