Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm
Abstract A novel deep-learning algorithm for artificial neural networks (ANNs), completely different from the back-propagation method, was developed in a previous study. The purpose of this study was to assess the feasibility of using the algorithm for the detection of intracranial haemorrhage (ICH)...
Guardado en:
Autores principales: | Ji Young Lee, Jong Soo Kim, Tae Yoon Kim, Young Soo Kim |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/0229c92f472942f5af0fe4ede49c86a9 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
por: Thi Mai Nguyen, et al.
Publicado: (2021) -
Performance testing of a novel deep learning algorithm for the detection of intracranial hemorrhage and first trial under clinical conditions
por: Philipp Gruschwitz, et al.
Publicado: (2021) -
Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images
por: Ye Rang Park, et al.
Publicado: (2021) -
Deep learning classification of lung cancer histology using CT images
por: Tafadzwa L. Chaunzwa, et al.
Publicado: (2021) -
A deep learning algorithm for automated measurement of vertebral body compression from X-ray images
por: Jae Won Seo, et al.
Publicado: (2021)