Multi-Class Classification of Lung Diseases Using CNN Models
In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang U...
Gespeichert in:
Veröffentlicht in: | Applied sciences 2021-10, Vol.11 (19), p.9289 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 19 |
container_start_page | 9289 |
container_title | Applied sciences |
container_volume | 11 |
creator | Hong, Min Rim, Beanbonyka Lee, Hongchang Jang, Hyeonung Oh, Joonho Choi, Seongjun |
description | In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s. |
doi_str_mv | 10.3390/app11199289 |
format | Article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_crossref_primary_10_3390_app11199289</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_5592ace0c3134993b04f050ee74dc9bf</doaj_id><sourcerecordid>2580965305</sourcerecordid><originalsourceid>FETCH-LOGICAL-c364t-4e09ca74e7aeeb8658edbb733c0339d7345c2428bc216fba6033f31d62cc9563</originalsourceid><addsrcrecordid>eNpNUMtKw0AUHUTBUrvyBwIuJTrvZMCNxFchrZu6HiaTO2VK7MSZZOHfG1uR3sV9HA7nHg5C1wTfMabwvel7QohStFRnaEZxIXPGSXF-sl-iRUo7PJUirCR4hh5WYzf4vOpMStmhe-etGXzYZ8Fl9bjfZk8-gUmQso_kp7Nar7NVaKFLV-jCmS7B4m_O0ebleVO95fX767J6rHPLJB9yDlhZU3AoDEBTSlFC2zQFYxZPxtuCcWEpp2VjKZGuMXKCHSOtpNYqIdkcLY-ybTA73Uf_aeK3DsbrAxDiVps4eNuBFkJRYwFbRhhXijWYOywwQMFbqxo3ad0ctfoYvkZIg96FMe4n95qKEispGBYT6_bIsjGkFMH9fyVY_4atT8JmP0lJb14</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2580965305</pqid></control><display><type>article</type><title>Multi-Class Classification of Lung Diseases Using CNN Models</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>Directory of Open Access Journals</source><source>EZB Electronic Journals Library</source><creator>Hong, Min ; Rim, Beanbonyka ; Lee, Hongchang ; Jang, Hyeonung ; Oh, Joonho ; Choi, Seongjun</creator><creatorcontrib>Hong, Min ; Rim, Beanbonyka ; Lee, Hongchang ; Jang, Hyeonung ; Oh, Joonho ; Choi, Seongjun</creatorcontrib><description>In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.</description><identifier>ISSN: 2076-3417</identifier><identifier>EISSN: 2076-3417</identifier><identifier>DOI: 10.3390/app11199289</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Bacterial infections ; Benchmarks ; Classification ; Coronaviruses ; COVID-19 ; Datasets ; Deep learning ; efficientnet ; Fever ; Image classification ; Learning ; Lung diseases ; Lungs ; Medical imaging ; multi-class classification ; Neural networks ; Pain ; Pneumonia ; Pneumothorax ; Tuberculosis</subject><ispartof>Applied sciences, 2021-10, Vol.11 (19), p.9289</ispartof><rights>2021 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-4e09ca74e7aeeb8658edbb733c0339d7345c2428bc216fba6033f31d62cc9563</citedby><cites>FETCH-LOGICAL-c364t-4e09ca74e7aeeb8658edbb733c0339d7345c2428bc216fba6033f31d62cc9563</cites><orcidid>0000-0001-9963-5521 ; 0000-0002-3517-6175 ; 0000-0003-1232-0610</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,2102,27924,27925</link.rule.ids></links><search><creatorcontrib>Hong, Min</creatorcontrib><creatorcontrib>Rim, Beanbonyka</creatorcontrib><creatorcontrib>Lee, Hongchang</creatorcontrib><creatorcontrib>Jang, Hyeonung</creatorcontrib><creatorcontrib>Oh, Joonho</creatorcontrib><creatorcontrib>Choi, Seongjun</creatorcontrib><title>Multi-Class Classification of Lung Diseases Using CNN Models</title><title>Applied sciences</title><description>In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.</description><subject>Accuracy</subject><subject>Bacterial infections</subject><subject>Benchmarks</subject><subject>Classification</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>efficientnet</subject><subject>Fever</subject><subject>Image classification</subject><subject>Learning</subject><subject>Lung diseases</subject><subject>Lungs</subject><subject>Medical imaging</subject><subject>multi-class classification</subject><subject>Neural networks</subject><subject>Pain</subject><subject>Pneumonia</subject><subject>Pneumothorax</subject><subject>Tuberculosis</subject><issn>2076-3417</issn><issn>2076-3417</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNpNUMtKw0AUHUTBUrvyBwIuJTrvZMCNxFchrZu6HiaTO2VK7MSZZOHfG1uR3sV9HA7nHg5C1wTfMabwvel7QohStFRnaEZxIXPGSXF-sl-iRUo7PJUirCR4hh5WYzf4vOpMStmhe-etGXzYZ8Fl9bjfZk8-gUmQso_kp7Nar7NVaKFLV-jCmS7B4m_O0ebleVO95fX767J6rHPLJB9yDlhZU3AoDEBTSlFC2zQFYxZPxtuCcWEpp2VjKZGuMXKCHSOtpNYqIdkcLY-ybTA73Uf_aeK3DsbrAxDiVps4eNuBFkJRYwFbRhhXijWYOywwQMFbqxo3ad0ctfoYvkZIg96FMe4n95qKEispGBYT6_bIsjGkFMH9fyVY_4atT8JmP0lJb14</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Hong, Min</creator><creator>Rim, Beanbonyka</creator><creator>Lee, Hongchang</creator><creator>Jang, Hyeonung</creator><creator>Oh, Joonho</creator><creator>Choi, Seongjun</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-9963-5521</orcidid><orcidid>https://orcid.org/0000-0002-3517-6175</orcidid><orcidid>https://orcid.org/0000-0003-1232-0610</orcidid></search><sort><creationdate>20211001</creationdate><title>Multi-Class Classification of Lung Diseases Using CNN Models</title><author>Hong, Min ; Rim, Beanbonyka ; Lee, Hongchang ; Jang, Hyeonung ; Oh, Joonho ; Choi, Seongjun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-4e09ca74e7aeeb8658edbb733c0339d7345c2428bc216fba6033f31d62cc9563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Bacterial infections</topic><topic>Benchmarks</topic><topic>Classification</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>efficientnet</topic><topic>Fever</topic><topic>Image classification</topic><topic>Learning</topic><topic>Lung diseases</topic><topic>Lungs</topic><topic>Medical imaging</topic><topic>multi-class classification</topic><topic>Neural networks</topic><topic>Pain</topic><topic>Pneumonia</topic><topic>Pneumothorax</topic><topic>Tuberculosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hong, Min</creatorcontrib><creatorcontrib>Rim, Beanbonyka</creatorcontrib><creatorcontrib>Lee, Hongchang</creatorcontrib><creatorcontrib>Jang, Hyeonung</creatorcontrib><creatorcontrib>Oh, Joonho</creatorcontrib><creatorcontrib>Choi, Seongjun</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Directory of Open Access Journals</collection><jtitle>Applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hong, Min</au><au>Rim, Beanbonyka</au><au>Lee, Hongchang</au><au>Jang, Hyeonung</au><au>Oh, Joonho</au><au>Choi, Seongjun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-Class Classification of Lung Diseases Using CNN Models</atitle><jtitle>Applied sciences</jtitle><date>2021-10-01</date><risdate>2021</risdate><volume>11</volume><issue>19</issue><spage>9289</spage><pages>9289-</pages><issn>2076-3417</issn><eissn>2076-3417</eissn><abstract>In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/app11199289</doi><orcidid>https://orcid.org/0000-0001-9963-5521</orcidid><orcidid>https://orcid.org/0000-0002-3517-6175</orcidid><orcidid>https://orcid.org/0000-0003-1232-0610</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2076-3417 |
ispartof | Applied sciences, 2021-10, Vol.11 (19), p.9289 |
issn | 2076-3417 2076-3417 |
language | eng |
recordid | cdi_crossref_primary_10_3390_app11199289 |
source | MDPI - Multidisciplinary Digital Publishing Institute; Directory of Open Access Journals; EZB Electronic Journals Library |
subjects | Accuracy Bacterial infections Benchmarks Classification Coronaviruses COVID-19 Datasets Deep learning efficientnet Fever Image classification Learning Lung diseases Lungs Medical imaging multi-class classification Neural networks Pain Pneumonia Pneumothorax Tuberculosis |
title | Multi-Class Classification of Lung Diseases Using CNN Models |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T15%3A10%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multi-Class%20Classification%20of%20Lung%20Diseases%20Using%20CNN%20Models&rft.jtitle=Applied%20sciences&rft.au=Hong,%20Min&rft.date=2021-10-01&rft.volume=11&rft.issue=19&rft.spage=9289&rft.pages=9289-&rft.issn=2076-3417&rft.eissn=2076-3417&rft_id=info:doi/10.3390/app11199289&rft_dat=%3Cproquest_doaj_%3E2580965305%3C/proquest_doaj_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2580965305&rft_id=info:pmid/&rft_doaj_id=oai_doaj_org_article_5592ace0c3134993b04f050ee74dc9bf&rfr_iscdi=true |