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Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?
The results show that a sufficiently complex model such as our three-dimensional version of the ALEXNET can effectively account for spatial differences, achieving a diagnosis accuracy of 94. 1% with an area under the ROC curve of 0. 984.
Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT images
The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world.
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