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)...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ji Young Lee, Jong Soo Kim, Tae Yoon Kim, Young Soo Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0229c92f472942f5af0fe4ede49c86a9
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0229c92f472942f5af0fe4ede49c86a9
record_format	dspace
spelling	oai:doaj.org-article:0229c92f472942f5af0fe4ede49c86a92021-12-02T12:33:45ZDetection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm10.1038/s41598-020-77441-z2045-2322https://doaj.org/article/0229c92f472942f5af0fe4ede49c86a92020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77441-zhttps://doaj.org/toc/2045-2322Abstract 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) and the classification of its subtypes, without employing the convolutional neural network (CNN). For the detection of ICH with the summation of all the computed tomography (CT) images for each case, the area under the ROC curve (AUC) was 0.859, and the sensitivity and the specificity were 78.0% and 80.0%, respectively. Regarding ICH localisation, CT images were divided into 10 subdivisions based on the intracranial height. With the subdivision of 41–50%, the best diagnostic performance for detecting ICH was obtained with AUC of 0.903, the sensitivity of 82.5%, and the specificity of 84.1%. For the classification of the ICH to subtypes, the accuracy rate for subarachnoid haemorrhage (SAH) was considerably excellent at 91.7%. This study revealed that our approach can greatly reduce the ICH diagnosis time in an actual emergency situation with a fairly good diagnostic performance.Ji Young LeeJong Soo KimTae Yoon KimYoung Soo KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-7 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ji Young Lee Jong Soo Kim Tae Yoon Kim Young Soo Kim Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm
description	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) and the classification of its subtypes, without employing the convolutional neural network (CNN). For the detection of ICH with the summation of all the computed tomography (CT) images for each case, the area under the ROC curve (AUC) was 0.859, and the sensitivity and the specificity were 78.0% and 80.0%, respectively. Regarding ICH localisation, CT images were divided into 10 subdivisions based on the intracranial height. With the subdivision of 41–50%, the best diagnostic performance for detecting ICH was obtained with AUC of 0.903, the sensitivity of 82.5%, and the specificity of 84.1%. For the classification of the ICH to subtypes, the accuracy rate for subarachnoid haemorrhage (SAH) was considerably excellent at 91.7%. This study revealed that our approach can greatly reduce the ICH diagnosis time in an actual emergency situation with a fairly good diagnostic performance.
format	article
author	Ji Young Lee Jong Soo Kim Tae Yoon Kim Young Soo Kim
author_facet	Ji Young Lee Jong Soo Kim Tae Yoon Kim Young Soo Kim
author_sort	Ji Young Lee
title	Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm
title_short	Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm
title_full	Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm
title_fullStr	Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm
title_full_unstemmed	Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm
title_sort	detection and classification of intracranial haemorrhage on ct images using a novel deep-learning algorithm
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/0229c92f472942f5af0fe4ede49c86a9
work_keys_str_mv	AT jiyounglee detectionandclassificationofintracranialhaemorrhageonctimagesusinganoveldeeplearningalgorithm AT jongsookim detectionandclassificationofintracranialhaemorrhageonctimagesusinganoveldeeplearningalgorithm AT taeyoonkim detectionandclassificationofintracranialhaemorrhageonctimagesusinganoveldeeplearningalgorithm AT youngsookim detectionandclassificationofintracranialhaemorrhageonctimagesusinganoveldeeplearningalgorithm
_version_	1718393839630155776

Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm

Ejemplares similares