Deep Siamese Networks with Bayesian Non-parametrics for Video Object Tracking

Rhodes, Anthony D.; Goel, Manan

doi:10.1007/978-3-030-32523-7_71

Cited by 1 publication

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Siamese neural network [1,4] is one type of neural network model that works well under this limitation. Siamese neural network was first presented by [4] for signature verification, and this work was later extended for text similarity [8], face recognition [9,10], video object tracking [11], and other image classification work [1,12]. is work is motivated by the importance of Parkinson's disease (PD), which is a neurodegenerative disease (NDD) that has motor-related symptoms, like tremor and instability [13].…”

Section: Introductionmentioning

confidence: 99%

Data Clustering Improves Siamese Neural Networks Classification of Parkinson’s Disease

2021

View full text Add to dashboard Cite

Parkinson’s disease (PD) is a clinical neurodegenerative disease having symptoms like tremor, rigidity, and postural disability. According to Harvard, about 60,000 of American citizens are diagnosed with PD yearly, with more than 10 million people infected worldwide. An estimate of 4% of the people have PD before they reach the age 50; however, the incident increases with age. Diagnosis of PD relies on the expertise of the physician and depends on several established clinical criteria. This makes the diagnosis subjective and inefficient. Hence, continuous efforts are being made to enhance the diagnosis of PD using deep learning approaches that rely on experienced neurologists. Siamese neural networks mainly work on two different input vectors and are used in comparison of output vectors. Moreover, clustering a dataset before applying classification enhances the distribution of similar samples among groups. In addition, applying the Siamese network can overcome the limitation of samples per class in the dataset by guiding the network to learn differences between samples rather than focusing on learning specific classes. In this paper, a Siamese neural network is applied to diagnose PD. Siamese networks predict the sample class by estimating how similar a sample is to other samples. The idea behind this work is clustering the dataset before training the network, as different pairs that belong to the same cluster are candidates to be mistaken by the network and assumed to be matched pairs. To overcome this problem, the dataset is first clustered, and then the architecture feeds the network to pairs of the same cluster. The proposed framework is concerned with comparing the performance when using clustered against unclustered data. The proposed framework outperforms the conventional framework without clustering. The accuracy achieved for classifying unclustered PD patients reached 76.75%, while it reached 84.02% for clustered data, outperforming the same technique on unclustered data. The significance of this study is in the enhanced performance achieved due to the clustering of data, which shows a promising framework to enhance the diagnostic capability of computer-aided disease diagnostic tools.

show abstract