ArticleLogic journal of the IGPL2018
Using deep neural networks along with dimensionality reduction techniques to assist the diagnosis of neurodegenerative disorders.
Article in Logic journal of the IGPL, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 32 citations in OpenAlex.
- Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders.Neural regeneration research · 2023Review
- Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review.Annals of translational medicine · 2021Review
- Identifying indicator species in ecological habitats using Deep Optimal Feature Learning.PloS one · 2021Article
- Editorial: Deep Learning in Aging Neuroscience.Frontiers in neuroinformatics · 2020Article
- Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics.European journal of nuclear medicine and molecular imaging · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The analysis of neuroimaging data is frequently used to assist the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) or Parkinson's disease (PD) and has become a routine procedure in the clinical practice. During the past decade, the pattern recognition community has proposed a number of machine learning-based systems that automatically analyse neuroimaging data in order to improve the diagnosis. However, the high dimensionality of the data is still a challenge and there is room for improvement. The development of novel classification frameworks as TensorFlow, recently released as open source by Google Inc., represents an opportunity to continue evolving these systems. In this work, we demonstrate several computer-aided diagnosis (CAD) systems based on Deep Neural Networks that improve the diagnosis for AD and PD and outperform those based on classical classifiers. In order to address the small sample size problem we evaluate two dimensionality reduction algorithms based on Principal Component Analysis and Non-Negative Matrix Factorization (NNMF), respectively. The performance of developed CAD systems is assessed using 4 datasets with neuroimaging data of different modalities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.