Convolutional Deep Belief Network with Feature Encoding for Classification of Neuroblastoma Histological Images

Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visua...

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Vydané v:	Journal of pathology informatics Ročník 9; číslo 1; s. 17
Hlavní autori:	Gheisari, Soheila, Catchpoole, Daniel R., Charlton, Amanda, Kennedy, Paul J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 01.01.2018 Wolters Kluwer India Pvt. Ltd Medknow Publications & Media Pvt. Ltd Medknow Publications & Media Pvt Ltd Elsevier
Predmet:	Accuracy Algorithms Architecture Belief networks Breast cancer Children Classification Coding Convolutional deep belief network Datasets Digital imaging feature encoding Feature extraction histological images Histology Image classification Image resolution Learning Level (quantity) Machine learning Methods Neuroblastoma Support vector machines Tumors neuroblastoma Convolutional deep belief network histological images feature encoding
ISSN:	2153-3539, 2229-5089, 2153-3539
On-line prístup:	Získať plný text
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