Deep learning and traditional-based CAD schemes for the pulmonary embolism diagnosis: A survey

Nowadays, pulmonary Computed Tomography Angiography (CTA) is the main tool for detecting Pulmonary Embolism (PE). However, manual interpretation of CTA volume requires a radiologist, which is time-consuming and error-prone due to the specific conditions of lung tissue, large volume of data, lack of...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Hosseini, Seyed Hesamoddin, Taherinia, Amir Hossein, Saadatmand, Mahdi
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Hosseini, Seyed Hesamoddin
Taherinia, Amir Hossein
Saadatmand, Mahdi
description Nowadays, pulmonary Computed Tomography Angiography (CTA) is the main tool for detecting Pulmonary Embolism (PE). However, manual interpretation of CTA volume requires a radiologist, which is time-consuming and error-prone due to the specific conditions of lung tissue, large volume of data, lack of experience, and eye fatigue. Therefore, Computer-Aided Design (CAD) systems are used as a second opinion for the diagnosis of PE. The purpose of this article is to review, evaluate, and compare the performance of deep learning and traditional-based CAD system for diagnosis PE and to help physicians and researchers in this field. In this study, all articles available in databases such as IEEE, ScienceDirect, Wiley, Springer, Nature, and Wolters Kluwer in the field of PE diagnosis were examined using traditional and deep learning methods. From 2002 to 2023, 23 papers were studied to extract the articles with the considered limitations. Each paper presents an automatic PE detection system that we evaluate using criteria such as sensitivity, False Positives (FP), and the number of datasets. This research work includes recent studies, state-of-the-art research works, and a more comprehensive overview compared to previously published review articles in this research area.
doi_str_mv 10.48550/arxiv.2312.01351
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2312_01351</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2312_01351</sourcerecordid><originalsourceid>FETCH-LOGICAL-a671-6d0b7b82f1135be0b9762ae720c5ca64b776197efd03e6abcc8b8348f3e5b37b3</originalsourceid><addsrcrecordid>eNotj8tOwzAQRb1hgQofwIr5gQQ7TuyUXZTykiqx6ZpoJp60lvKSnVb07ymF1d0cXZ0jxIOSaV4WhXzC8O1PaaZVlkqlC3UrvjbMM_SMYfTjHnB0sAR0fvHTiH1CGNlBXW0gtgceOEI3BVgODPOxHy5IOAMPNPU-DuA87scp-vgMFcRjOPH5Ttx02Ee-_9-V2L2-7Or3ZPv59lFX2wSNVYlxkiyVWacuUsSS1tZkyDaTbdGiyclao9aWOyc1G6S2LanUedlpLkhb0ivx-Hd7DWzm4IeLWfMb2lxD9Q8GYk7R</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Deep learning and traditional-based CAD schemes for the pulmonary embolism diagnosis: A survey</title><source>arXiv.org</source><creator>Hosseini, Seyed Hesamoddin ; Taherinia, Amir Hossein ; Saadatmand, Mahdi</creator><creatorcontrib>Hosseini, Seyed Hesamoddin ; Taherinia, Amir Hossein ; Saadatmand, Mahdi</creatorcontrib><description>Nowadays, pulmonary Computed Tomography Angiography (CTA) is the main tool for detecting Pulmonary Embolism (PE). However, manual interpretation of CTA volume requires a radiologist, which is time-consuming and error-prone due to the specific conditions of lung tissue, large volume of data, lack of experience, and eye fatigue. Therefore, Computer-Aided Design (CAD) systems are used as a second opinion for the diagnosis of PE. The purpose of this article is to review, evaluate, and compare the performance of deep learning and traditional-based CAD system for diagnosis PE and to help physicians and researchers in this field. In this study, all articles available in databases such as IEEE, ScienceDirect, Wiley, Springer, Nature, and Wolters Kluwer in the field of PE diagnosis were examined using traditional and deep learning methods. From 2002 to 2023, 23 papers were studied to extract the articles with the considered limitations. Each paper presents an automatic PE detection system that we evaluate using criteria such as sensitivity, False Positives (FP), and the number of datasets. This research work includes recent studies, state-of-the-art research works, and a more comprehensive overview compared to previously published review articles in this research area.</description><identifier>DOI: 10.48550/arxiv.2312.01351</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2023-12</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2312.01351$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2312.01351$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Hosseini, Seyed Hesamoddin</creatorcontrib><creatorcontrib>Taherinia, Amir Hossein</creatorcontrib><creatorcontrib>Saadatmand, Mahdi</creatorcontrib><title>Deep learning and traditional-based CAD schemes for the pulmonary embolism diagnosis: A survey</title><description>Nowadays, pulmonary Computed Tomography Angiography (CTA) is the main tool for detecting Pulmonary Embolism (PE). However, manual interpretation of CTA volume requires a radiologist, which is time-consuming and error-prone due to the specific conditions of lung tissue, large volume of data, lack of experience, and eye fatigue. Therefore, Computer-Aided Design (CAD) systems are used as a second opinion for the diagnosis of PE. The purpose of this article is to review, evaluate, and compare the performance of deep learning and traditional-based CAD system for diagnosis PE and to help physicians and researchers in this field. In this study, all articles available in databases such as IEEE, ScienceDirect, Wiley, Springer, Nature, and Wolters Kluwer in the field of PE diagnosis were examined using traditional and deep learning methods. From 2002 to 2023, 23 papers were studied to extract the articles with the considered limitations. Each paper presents an automatic PE detection system that we evaluate using criteria such as sensitivity, False Positives (FP), and the number of datasets. This research work includes recent studies, state-of-the-art research works, and a more comprehensive overview compared to previously published review articles in this research area.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tOwzAQRb1hgQofwIr5gQQ7TuyUXZTykiqx6ZpoJp60lvKSnVb07ymF1d0cXZ0jxIOSaV4WhXzC8O1PaaZVlkqlC3UrvjbMM_SMYfTjHnB0sAR0fvHTiH1CGNlBXW0gtgceOEI3BVgODPOxHy5IOAMPNPU-DuA87scp-vgMFcRjOPH5Ttx02Ee-_9-V2L2-7Or3ZPv59lFX2wSNVYlxkiyVWacuUsSS1tZkyDaTbdGiyclao9aWOyc1G6S2LanUedlpLkhb0ivx-Hd7DWzm4IeLWfMb2lxD9Q8GYk7R</recordid><startdate>20231203</startdate><enddate>20231203</enddate><creator>Hosseini, Seyed Hesamoddin</creator><creator>Taherinia, Amir Hossein</creator><creator>Saadatmand, Mahdi</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20231203</creationdate><title>Deep learning and traditional-based CAD schemes for the pulmonary embolism diagnosis: A survey</title><author>Hosseini, Seyed Hesamoddin ; Taherinia, Amir Hossein ; Saadatmand, Mahdi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a671-6d0b7b82f1135be0b9762ae720c5ca64b776197efd03e6abcc8b8348f3e5b37b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Hosseini, Seyed Hesamoddin</creatorcontrib><creatorcontrib>Taherinia, Amir Hossein</creatorcontrib><creatorcontrib>Saadatmand, Mahdi</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hosseini, Seyed Hesamoddin</au><au>Taherinia, Amir Hossein</au><au>Saadatmand, Mahdi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep learning and traditional-based CAD schemes for the pulmonary embolism diagnosis: A survey</atitle><date>2023-12-03</date><risdate>2023</risdate><abstract>Nowadays, pulmonary Computed Tomography Angiography (CTA) is the main tool for detecting Pulmonary Embolism (PE). However, manual interpretation of CTA volume requires a radiologist, which is time-consuming and error-prone due to the specific conditions of lung tissue, large volume of data, lack of experience, and eye fatigue. Therefore, Computer-Aided Design (CAD) systems are used as a second opinion for the diagnosis of PE. The purpose of this article is to review, evaluate, and compare the performance of deep learning and traditional-based CAD system for diagnosis PE and to help physicians and researchers in this field. In this study, all articles available in databases such as IEEE, ScienceDirect, Wiley, Springer, Nature, and Wolters Kluwer in the field of PE diagnosis were examined using traditional and deep learning methods. From 2002 to 2023, 23 papers were studied to extract the articles with the considered limitations. Each paper presents an automatic PE detection system that we evaluate using criteria such as sensitivity, False Positives (FP), and the number of datasets. This research work includes recent studies, state-of-the-art research works, and a more comprehensive overview compared to previously published review articles in this research area.</abstract><doi>10.48550/arxiv.2312.01351</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.2312.01351
ispartof
issn
language eng
recordid cdi_arxiv_primary_2312_01351
source arXiv.org
subjects Computer Science - Computer Vision and Pattern Recognition
title Deep learning and traditional-based CAD schemes for the pulmonary embolism diagnosis: A survey
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T00%3A06%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Deep%20learning%20and%20traditional-based%20CAD%20schemes%20for%20the%20pulmonary%20embolism%20diagnosis:%20A%20survey&rft.au=Hosseini,%20Seyed%20Hesamoddin&rft.date=2023-12-03&rft_id=info:doi/10.48550/arxiv.2312.01351&rft_dat=%3Carxiv_GOX%3E2312_01351%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true