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RMDL: Classification of Parkinson's disease by nature-inspired Algorithm
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| Author:
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M.NAGARAJU
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| Abstract:
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Nowadays Parkinson’s disease (PD) is suffered by many people; it is a chronic and progressive illness throughout the world. This disease can be easily identified when patients come complaining about the symptoms such as tremors, the slowness of movement and freezing-of-gait. In this work, we study computerized analysis of PD and illustrate a technique to classify the diverse features of deep brain surgical images using the machine learning techniques. The feature of the deep brain can be analyzed and their features can be recognized with the help of image processing techniques. In this work, we implemented a deep learning classification technique called Random Multimodal Deep Learning (RMDL). With this technique we attain better stability and reliability through collections of deep learning methods. This approach paves a way to take diverse data as input such as manuscript, video, pictures, and emblematic. In this work, we analyzed brain images of Parkinson disease with help of RMDL. The obtained results generate consistently enhanced performance than model techniques applying on the large number of data types and categorization problems.
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| Keyword:
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deep learning, classification, RMDL, brain, performance, text, Metaheuristic, swarm optimization.
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.03.001
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| Download:
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