Cargando…
A Novel Solution Based on Scale Invariant Feature Transform Descriptors and Deep Learning for the Detection of Suspicious Regions in Mammogram Images
BACKGROUND: Deep learning methods have become popular for their high-performance rate in the classification and detection of events in computer vision tasks. Transfer learning paradigm is widely adopted to apply pretrained convolutional neural network (CNN) on medical domains overcoming the problem...
Autores principales: | Bruno, Alessandro, Ardizzone, Edoardo, Vitabile, Salvatore, Midiri, Massimo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528986/ https://www.ncbi.nlm.nih.gov/pubmed/33062608 http://dx.doi.org/10.4103/jmss.JMSS_31_19 |
Ejemplares similares
-
A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms
por: Oza, Parita, et al.
Publicado: (2021) -
Retinal image mosaicking using scale-invariant feature transformation feature descriptors and Voronoi diagram (Erratum)
por: Jalili, Jalil, et al.
Publicado: (2020) -
Novel Texture Feature Descriptors Based on Multi-Fractal Analysis and LBP for Classifying Breast Density in Mammograms
por: Li, Haipeng, et al.
Publicado: (2021) -
Simplifying Breast Imaging Reporting and Data System classification of mammograms with pure suspicious calcifications
por: Menezes, Gisela LG, et al.
Publicado: (2017) -
Scale invariant texture descriptors for classifying celiac disease
por: Hegenbart, Sebastian, et al.
Publicado: (2013)