Predicting malignant nodules by fusing deep features with classical radiomics features

Volume: 5, Issue: 01, Pages: 1 - 1
Published: Mar 21, 2018
Abstract
Lung cancer has a high incidence and mortality rate. Early detection and diagnosis of lung cancers is best achieved with low-dose computed tomography (CT). Classical radiomics features extracted from lung CT images have been shown as able to predict cancer incidence and prognosis. With the advancement of deep learning and convolutional neural networks (CNNs), deep features can be identified to analyze lung CTs for prognosis prediction and...
Paper Details
Title
Predicting malignant nodules by fusing deep features with classical radiomics features
Published Date
Mar 21, 2018
Volume
5
Issue
01
Pages
1 - 1
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.