Parkinson's Disease Detection using Convolutional Neural Networks
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 6, Pages 1298-1307
AbstractThese days, a significant research exertion in social insurance biometrics is finding exact biomarkers that permit creating clinical choice help instruments. These instruments aid with the diagnosis and treatment of diseases such as Parkinson's disease. In this article, a convolutionary neural network (CNN) for the PD identification from drawing production is broken. This CNN comprises two parts: extraction and arranging (completely linked layers). CNN involves two pieces. CNN refers to the increase in frequency volume from 0 Hz to 25 Hz by the Fast Fourier Module. Throughout the modeling cycle the separating capacity of various headings tested achieved the greatest outcomes for both X & Y rollers. This research has been conducted using open database: a digital image tablet dataset from Parkinson Spiral Drawings. This study produced 96.5 percent of precision, 97.7 percent of F1 and 99.2 percent of region. There were the strongest results.
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