Abstract
Parkinson’s disease (PD) is an incurable, neurodegenerative disease, and its early diagnosis can be done with the aid of modality magnetic resonance imaging (MRI). Biomarkers enable the easy and accurate diagnosis of this disease, and thereafter, the proper treatment can help to control the progressive loss of dopaminergic neurons. Deep learning approaches such as convolutional neural networks (CNN) are gaining much momentum recently and heavily being used in the medical domain for detecting useful patterns relevant to diseases. In this work, we are trying to develop the genetic algorithm (GA-based segmentation technique incorporated with deep CNN models for the easy and accurate diagnosis of PD. Experimental results show that the proposed approach outperforms the state-of-the-art for classifying Parkinson’s disease from MRI.
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Sreelakshmi, S., Mathew, R. (2022). A Hybrid Approach for Classifying Parkinson’s Disease from Brain MRI. In: Ullah, A., Anwar, S., Rocha, Á., Gill, S. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 350. Springer, Singapore. https://doi.org/10.1007/978-981-16-7618-5_15
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DOI: https://doi.org/10.1007/978-981-16-7618-5_15
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