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An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks
Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarac...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046213/ https://www.ncbi.nlm.nih.gov/pubmed/36979210 http://dx.doi.org/10.3390/brainsci13030400 |
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author | Rajagopal, Manikandan Buradagunta, Suvarna Almeshari, Meshari Alzamil, Yasser Ramalingam, Rajakumar Ravi, Vinayakumar |
author_facet | Rajagopal, Manikandan Buradagunta, Suvarna Almeshari, Meshari Alzamil, Yasser Ramalingam, Rajakumar Ravi, Vinayakumar |
author_sort | Rajagopal, Manikandan |
collection | PubMed |
description | Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient’s skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms. |
format | Online Article Text |
id | pubmed-10046213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100462132023-03-29 An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks Rajagopal, Manikandan Buradagunta, Suvarna Almeshari, Meshari Alzamil, Yasser Ramalingam, Rajakumar Ravi, Vinayakumar Brain Sci Article Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient’s skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms. MDPI 2023-02-25 /pmc/articles/PMC10046213/ /pubmed/36979210 http://dx.doi.org/10.3390/brainsci13030400 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rajagopal, Manikandan Buradagunta, Suvarna Almeshari, Meshari Alzamil, Yasser Ramalingam, Rajakumar Ravi, Vinayakumar An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks |
title | An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks |
title_full | An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks |
title_fullStr | An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks |
title_full_unstemmed | An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks |
title_short | An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks |
title_sort | efficient framework to detect intracranial hemorrhage using hybrid deep neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046213/ https://www.ncbi.nlm.nih.gov/pubmed/36979210 http://dx.doi.org/10.3390/brainsci13030400 |
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