Web application for ICH subtype classification from CT head scans

Traumatic brain injury (TBI) causes intracranial hemorrhage (ICH) that requires urgent diagnosis and treatment to improve patient outcome. Machine learning techniques can help clinicians to classify brain lesions and assist clinicians diagnose TBI from radiological scans. The project objective was t...

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Main Author: Lim, Candy
Other Authors: Jagath C Rajapakse
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/156777
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1567772022-05-12T11:36:28Z Web application for ICH subtype classification from CT head scans Lim, Candy Jagath C Rajapakse School of Computer Science and Engineering ASJagath@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Computer science and engineering::Computer applications::Life and medical sciences Traumatic brain injury (TBI) causes intracranial hemorrhage (ICH) that requires urgent diagnosis and treatment to improve patient outcome. Machine learning techniques can help clinicians to classify brain lesions and assist clinicians diagnose TBI from radiological scans. The project objective was to build a CAD system which assists in the detection, screening, and diagnosis of ICH in routine clinical practice. The models are trained and created using different CNN models developed on Tensorflow, Keras, and OpenCV using sliced CT scanned images from the 2019-RSNA Brain CT Hemorrhage Challenge dataset. The results from these models were evaluated and the MobileNetV1 architecture model is determined to give the best performance analysis. The CAD system, which was constructed using the Django and ReactJS frameworks, was able to extract medical picture analysis for use in a deep learning solution. Bachelor of Engineering (Computer Science) 2022-04-23T12:36:45Z 2022-04-23T12:36:45Z 2022 Final Year Project (FYP) Lim, C. (2022). Web application for ICH subtype classification from CT head scans. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156777 https://hdl.handle.net/10356/156777 en 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::Artificial intelligence
Engineering::Computer science and engineering::Computer applications::Life and medical sciences
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Lim, Candy
Web application for ICH subtype classification from CT head scans
description Traumatic brain injury (TBI) causes intracranial hemorrhage (ICH) that requires urgent diagnosis and treatment to improve patient outcome. Machine learning techniques can help clinicians to classify brain lesions and assist clinicians diagnose TBI from radiological scans. The project objective was to build a CAD system which assists in the detection, screening, and diagnosis of ICH in routine clinical practice. The models are trained and created using different CNN models developed on Tensorflow, Keras, and OpenCV using sliced CT scanned images from the 2019-RSNA Brain CT Hemorrhage Challenge dataset. The results from these models were evaluated and the MobileNetV1 architecture model is determined to give the best performance analysis. The CAD system, which was constructed using the Django and ReactJS frameworks, was able to extract medical picture analysis for use in a deep learning solution.
author2 Jagath C Rajapakse
author_facet Jagath C Rajapakse
Lim, Candy
format Final Year Project
author Lim, Candy
author_sort Lim, Candy
title Web application for ICH subtype classification from CT head scans
title_short Web application for ICH subtype classification from CT head scans
title_full Web application for ICH subtype classification from CT head scans
title_fullStr Web application for ICH subtype classification from CT head scans
title_full_unstemmed Web application for ICH subtype classification from CT head scans
title_sort web application for ich subtype classification from ct head scans
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/156777
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