Performance Comparison of Individual and Ensemble CNN Models for the Classification of Brain 18F-FDG-PET Scans.
Tomomi NobashiClaudia ZachariasJason K EllisValentina FerriMary Ellen KoranBenjamin L FrancAndrei IagaruGuido A DavidzonPublished in: Journal of digital imaging (2021)
Data ensemble using different window settings and axes was effective to improve 2D-CNN performance parameters for the classification of brain FDG-PET scans. If prospectively validated with a larger cohort of patients, similar models could provide decision support in a clinical setting.
Keyphrases
- pet ct
- positron emission tomography
- computed tomography
- pet imaging
- convolutional neural network
- deep learning
- end stage renal disease
- machine learning
- resting state
- white matter
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- functional connectivity
- prognostic factors
- electronic health record
- magnetic resonance imaging
- contrast enhanced
- multiple sclerosis
- big data
- cerebral ischemia
- clinical evaluation