Detecting intracranial haemorrhages in brain CT scans using deep learning techniques

Intracranial Haemorrhage(ICH) is a life-threatening emergency that requires quick diagnosis and treatment. A Computed Tomography (CT) Scan is taken of a patient when they arrive at a hospital, but an expert radiologist is required to accurately locate the haemorrhage in the scan. In this project, we...

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Main Author: Vivek, Adrakatti
Other Authors: Jagath C Rajapakse
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/156755
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1567552022-04-23T13:27:08Z Detecting intracranial haemorrhages in brain CT scans using deep learning techniques Vivek, Adrakatti Jagath C Rajapakse School of Computer Science and Engineering Biomedical Informatics Lab ASJagath@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Intracranial Haemorrhage(ICH) is a life-threatening emergency that requires quick diagnosis and treatment. A Computed Tomography (CT) Scan is taken of a patient when they arrive at a hospital, but an expert radiologist is required to accurately locate the haemorrhage in the scan. In this project, we propose a novel algorithm for the detection and segmentation of the ICH in the Brain CT Scan Accurately. This will allow quick and accurate detection of ICH and assist doctors to take the necessary action. The algorithms have been implemented using Deep Learning Algorithms using PyTorch/Tensorflow libraries and using other libraries such as PyDicom and OpenCV. Bachelor of Engineering (Computer Engineering) 2022-04-23T11:12:33Z 2022-04-23T11:12:33Z 2022 Final Year Project (FYP) Vivek, A. (2022). Detecting intracranial haemorrhages in brain CT scans using deep learning techniques. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156755 https://hdl.handle.net/10356/156755 en SCSE21-0211 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Vivek, Adrakatti
Detecting intracranial haemorrhages in brain CT scans using deep learning techniques
description Intracranial Haemorrhage(ICH) is a life-threatening emergency that requires quick diagnosis and treatment. A Computed Tomography (CT) Scan is taken of a patient when they arrive at a hospital, but an expert radiologist is required to accurately locate the haemorrhage in the scan. In this project, we propose a novel algorithm for the detection and segmentation of the ICH in the Brain CT Scan Accurately. This will allow quick and accurate detection of ICH and assist doctors to take the necessary action. The algorithms have been implemented using Deep Learning Algorithms using PyTorch/Tensorflow libraries and using other libraries such as PyDicom and OpenCV.
author2 Jagath C Rajapakse
author_facet Jagath C Rajapakse
Vivek, Adrakatti
format Final Year Project
author Vivek, Adrakatti
author_sort Vivek, Adrakatti
title Detecting intracranial haemorrhages in brain CT scans using deep learning techniques
title_short Detecting intracranial haemorrhages in brain CT scans using deep learning techniques
title_full Detecting intracranial haemorrhages in brain CT scans using deep learning techniques
title_fullStr Detecting intracranial haemorrhages in brain CT scans using deep learning techniques
title_full_unstemmed Detecting intracranial haemorrhages in brain CT scans using deep learning techniques
title_sort detecting intracranial haemorrhages in brain ct scans using deep learning techniques
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/156755
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