Improving accuracy of Gaussian mixture model classifiers with additional discriminative training

Patrikar, Ajay M.; Baker, John P.

doi:10.1109/ijcnn.2016.7727399

Cited by 1 publication

(1 citation statement)

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“…Although the Gaussian kernel SVM gave good results, it may be enhanced further. As future work, we plan to investigate the use of Gaussian Mixture Classifier (GMM) [33]. In fact, GMM can model each class with several Gaussian and thus, can deal with variability of the data within each class.…”

Section: Discussionmentioning

confidence: 99%

Parkinson’s Disease Diagnosis using Spiral Test on Digital Tablets

Al-Yousef¹,

Saikhan²,

Gowaifly³

et al. 2020

IJACSA

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For a proper diagnosis, Parkinson's disease (PD) requires frequent visits to the doctor for physical tests, causing a huge burden on the patient. As PD impairs the handwriting ability, the handwriting pattern can be used as an indicator for PD diagnosis. More specifically, the Static Spiral Test (SST) and the Dynamic Spiral Test (DST), that consists in retracing spirals using digital pen. Such exam can be self-conducted by the patient, and thus it would be convenient and non-time-consuming for both the patient and the medical staff. In this project, we designed and implemented a system that automatically self-aiddiagnoses PD using SST and DST on digital tablets. The system includes two main components, image processing techniques to pre-process and extract the appropriate visual features and machine learning techniques to recognize PD automatically. The conducted experiment showed that the semi-local Edge Histogram Descriptor extracted from DST drawing, and conveyed to a Gaussian Kernel Support Vector Machine outperforms the other considered systems with an accuracy, specificity and sensitivity around 90%.

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Section: Discussionmentioning

confidence: 99%