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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...

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Autores principales: Rajagopal, Manikandan, Buradagunta, Suvarna, Almeshari, Meshari, Alzamil, Yasser, Ramalingam, Rajakumar, Ravi, Vinayakumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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