A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases
Chest X-ray (CXR) is one of the most common types of radiology examination for the diagnosis of thorax diseases. Computer-aided diagnosis (CAD) was developed to help radiologists to achieve diagnostic excellence in a short period of time and to enhance patient healthcare. In this paper, we seek to i...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.64279-64288 |
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| creator | Allaouzi, Imane Ben Ahmed, Mohamed |
| description | Chest X-ray (CXR) is one of the most common types of radiology examination for the diagnosis of thorax diseases. Computer-aided diagnosis (CAD) was developed to help radiologists to achieve diagnostic excellence in a short period of time and to enhance patient healthcare. In this paper, we seek to improve the performance of the CAD system in the task of thorax diseases diagnosis by providing a new method that combines the advantages of CNN models in image feature extraction with those of the problem transformation methods in the multi-label classification task. The experimental study is tested on two publicly available CXR datasets ChestX-ray14 (frontal view) and CheXpert (frontal and lateral views). The results show that our proposed method outperformed the current state of the art. |
| doi_str_mv | 10.1109/ACCESS.2019.2916849 |
| format | Article |
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Computer-aided diagnosis (CAD) was developed to help radiologists to achieve diagnostic excellence in a short period of time and to enhance patient healthcare. In this paper, we seek to improve the performance of the CAD system in the task of thorax diseases diagnosis by providing a new method that combines the advantages of CNN models in image feature extraction with those of the problem transformation methods in the multi-label classification task. The experimental study is tested on two publicly available CXR datasets ChestX-ray14 (frontal view) and CheXpert (frontal and lateral views). The results show that our proposed method outperformed the current state of the art.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2019.2916849</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Biomedical imaging ; CAD ; Chest ; Classification ; CNN ; computer vision ; CXR ; deep learning ; Diagnosis ; Diagnostic systems ; Diseases ; Feature extraction ; Image classification ; image feature extraction ; multi-label classification ; problem transformation method ; Radiology ; Solid modeling ; Task analysis ; thoracic pathologies ; Thorax ; transfer learning</subject><ispartof>IEEE access, 2019, Vol.7, p.64279-64288</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-1485db7c67a52937c5375ed3dad9ac92f0a21dfa30b9be3a854b2789a3f84f653</citedby><cites>FETCH-LOGICAL-c408t-1485db7c67a52937c5375ed3dad9ac92f0a21dfa30b9be3a854b2789a3f84f653</cites><orcidid>0000-0002-8737-8115</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8719904$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Allaouzi, Imane</creatorcontrib><creatorcontrib>Ben Ahmed, Mohamed</creatorcontrib><title>A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases</title><title>IEEE access</title><addtitle>Access</addtitle><description>Chest X-ray (CXR) is one of the most common types of radiology examination for the diagnosis of thorax diseases. 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The results show that our proposed method outperformed the current state of the art.</description><subject>Biomedical imaging</subject><subject>CAD</subject><subject>Chest</subject><subject>Classification</subject><subject>CNN</subject><subject>computer vision</subject><subject>CXR</subject><subject>deep learning</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Diseases</subject><subject>Feature extraction</subject><subject>Image classification</subject><subject>image feature extraction</subject><subject>multi-label classification</subject><subject>problem transformation method</subject><subject>Radiology</subject><subject>Solid modeling</subject><subject>Task analysis</subject><subject>thoracic pathologies</subject><subject>Thorax</subject><subject>transfer learning</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUdtKJDEQbURBUb_Al8A-95hrJ3kcWndXGHdhVfAtVOeyk6HHjEmPrH9vtEW2XqqoqnPqcprmguAFIVhfLvv--u5uQTHRC6pJp7g-aE4o6XTLBOsO_4uPm_NSNriaqikhT5qHJfqVXvyIlrtdTmDXKKSMbvfjFNsVDLXQr32Z0GP7B15RP0IpMUQLU0xPKAXUp-22RvfrlOEfuorFQ_HlrDkKMBZ__ulPm4fv1_f9z3b1-8dNv1y1lmM1tYQr4QZpOwmCaiatYFJ4xxw4DVbTgIESF4DhQQ-egRJ8oFJpYEHx0Al22tzMvC7Bxuxy3EJ-NQmi-Uik_NdAnqIdvfHgmNVcaYc73mkKmFArwXkdvFMSKte3mav-4XlfbzabtM9PdX1DuRAd4USo2sXmLptTKdmHr6kEm3c5zCyHeZfDfMpRURczKnrvvxBKEq0xZ28fq4Uz</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Allaouzi, Imane</creator><creator>Ben Ahmed, Mohamed</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Computer-aided diagnosis (CAD) was developed to help radiologists to achieve diagnostic excellence in a short period of time and to enhance patient healthcare. In this paper, we seek to improve the performance of the CAD system in the task of thorax diseases diagnosis by providing a new method that combines the advantages of CNN models in image feature extraction with those of the problem transformation methods in the multi-label classification task. The experimental study is tested on two publicly available CXR datasets ChestX-ray14 (frontal view) and CheXpert (frontal and lateral views). The results show that our proposed method outperformed the current state of the art.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2916849</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-8737-8115</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Biomedical imaging CAD Chest Classification CNN computer vision CXR deep learning Diagnosis Diagnostic systems Diseases Feature extraction Image classification image feature extraction multi-label classification problem transformation method Radiology Solid modeling Task analysis thoracic pathologies Thorax transfer learning |
| title | A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases |
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