Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions
Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative...
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| Veröffentlicht in: | BioMed research international 2020, Vol.2020 (2020), p.1-10 |
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| creator | Al Maghayreh, Eslam Mahmood, Awais Rahmany, Ines Dhahri, Habib Elkilani, Wail |
| description | Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques. |
| doi_str_mv | 10.1155/2020/4671349 |
| format | Article |
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The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2020/4671349</identifier><identifier>PMID: 32258124</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Algorithms ; Benign ; Breast - diagnostic imaging ; Breast - pathology ; Breast cancer ; Breast Neoplasms - classification ; Breast Neoplasms - diagnosis ; Breast Neoplasms - pathology ; Cancer ; Cell Proliferation ; Classification ; Data mining ; Datasets ; Deep learning ; Diagnosis ; Diagnosis, Computer-Assisted - methods ; Diagnostic systems ; Experiments ; Female ; Fibrocystic Breast Disease - classification ; Fibrocystic Breast Disease - diagnosis ; Fibrocystic Breast Disease - pathology ; Fourier transforms ; Gene expression ; Humans ; Image Interpretation, Computer-Assisted - methods ; Learning algorithms ; Lesions ; Machine Learning ; Mammography ; Medical diagnosis ; Metastasis ; Morphology ; Neoplasms - classification ; Neoplasms - diagnosis ; Neoplasms - pathology ; Neural networks ; Principal components analysis ; Regression analysis ; Review ; Support vector machines ; Tabu search ; Tumors ; Ultrasonic imaging ; Wavelet transforms</subject><ispartof>BioMed research international, 2020, Vol.2020 (2020), p.1-10</ispartof><rights>Copyright © 2020 Habib Dhahri et al.</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>Copyright © 2020 Habib Dhahri et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2020 Habib Dhahri et al. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c499t-38abe2f34beb4f064005129e43de0e1357dbf6aaa7c35394de12850d78b7044c3</citedby><cites>FETCH-LOGICAL-c499t-38abe2f34beb4f064005129e43de0e1357dbf6aaa7c35394de12850d78b7044c3</cites><orcidid>0000-0003-2436-0249 ; 0000-0003-4163-7625 ; 0000-0003-4668-7840 ; 0000-0001-9086-5080</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064857/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064857/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,4010,27900,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32258124$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Wei, Ji-Fu</contributor><creatorcontrib>Al Maghayreh, Eslam</creatorcontrib><creatorcontrib>Mahmood, Awais</creatorcontrib><creatorcontrib>Rahmany, Ines</creatorcontrib><creatorcontrib>Dhahri, Habib</creatorcontrib><creatorcontrib>Elkilani, Wail</creatorcontrib><title>Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Benign</subject><subject>Breast - diagnostic imaging</subject><subject>Breast - pathology</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - classification</subject><subject>Breast Neoplasms - diagnosis</subject><subject>Breast Neoplasms - pathology</subject><subject>Cancer</subject><subject>Cell Proliferation</subject><subject>Classification</subject><subject>Data mining</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Diagnosis</subject><subject>Diagnosis, Computer-Assisted - methods</subject><subject>Diagnostic systems</subject><subject>Experiments</subject><subject>Female</subject><subject>Fibrocystic Breast Disease - classification</subject><subject>Fibrocystic Breast Disease - diagnosis</subject><subject>Fibrocystic Breast Disease - pathology</subject><subject>Fourier transforms</subject><subject>Gene expression</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Learning algorithms</subject><subject>Lesions</subject><subject>Machine Learning</subject><subject>Mammography</subject><subject>Medical diagnosis</subject><subject>Metastasis</subject><subject>Morphology</subject><subject>Neoplasms - classification</subject><subject>Neoplasms - diagnosis</subject><subject>Neoplasms - pathology</subject><subject>Neural networks</subject><subject>Principal components analysis</subject><subject>Regression analysis</subject><subject>Review</subject><subject>Support vector machines</subject><subject>Tabu search</subject><subject>Tumors</subject><subject>Ultrasonic imaging</subject><subject>Wavelet transforms</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqNkU1v1DAQhiNERavSG2cUiQsShPrbyQWpXfElbQUS5WxNnPGuq6xd7KSIf49Xu2yhp87FI88z78zoraoXlLyjVMpzRhg5F0pTLron1QnjVDSKCvr0kHN-XJ3lfENKtFSRTj2rjjljsqVMnFTDNfRz_R0h2XUNYaivwK59wGZZvoIPq3oxQs7eeQuTj6GOrr7E4FdhT48lhTDV31IcvcNUqDusLxNCnuol5tKTn1dHDsaMZ_v3tPrx8cP14nOz_Prpy-Ji2VjRdVPDW-iROS567IUjShAiKetQ8AEJUi710DsFANpyyTsxIGWtJINue02EsPy0er_TvZ37DQ4Ww5RgNLfJbyD9NhG8-b8S_Nqs4p3RZVgrdRF4vRdI8eeMeTIbny2OIwSMczaMt1pJyQkr6KsH6E2cUyjnFUqX1VWx5Z5awYjGBxfLXLsVNReKCdV2rKOFerujbIo5J3SHlSkxW5_N1mez97ngL_898wD_dbUAb3ZAcXKAX_6RclgYdHBPlzJpNf8DkWS4QQ</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Al Maghayreh, Eslam</creator><creator>Mahmood, Awais</creator><creator>Rahmany, Ines</creator><creator>Dhahri, Habib</creator><creator>Elkilani, Wail</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2436-0249</orcidid><orcidid>https://orcid.org/0000-0003-4163-7625</orcidid><orcidid>https://orcid.org/0000-0003-4668-7840</orcidid><orcidid>https://orcid.org/0000-0001-9086-5080</orcidid></search><sort><creationdate>2020</creationdate><title>Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions</title><author>Al Maghayreh, Eslam ; Mahmood, Awais ; Rahmany, Ines ; Dhahri, Habib ; Elkilani, Wail</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c499t-38abe2f34beb4f064005129e43de0e1357dbf6aaa7c35394de12850d78b7044c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Benign</topic><topic>Breast - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Al Maghayreh, Eslam</au><au>Mahmood, Awais</au><au>Rahmany, Ines</au><au>Dhahri, Habib</au><au>Elkilani, Wail</au><au>Wei, Ji-Fu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. 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| subjects | Accuracy Algorithms Benign Breast - diagnostic imaging Breast - pathology Breast cancer Breast Neoplasms - classification Breast Neoplasms - diagnosis Breast Neoplasms - pathology Cancer Cell Proliferation Classification Data mining Datasets Deep learning Diagnosis Diagnosis, Computer-Assisted - methods Diagnostic systems Experiments Female Fibrocystic Breast Disease - classification Fibrocystic Breast Disease - diagnosis Fibrocystic Breast Disease - pathology Fourier transforms Gene expression Humans Image Interpretation, Computer-Assisted - methods Learning algorithms Lesions Machine Learning Mammography Medical diagnosis Metastasis Morphology Neoplasms - classification Neoplasms - diagnosis Neoplasms - pathology Neural networks Principal components analysis Regression analysis Review Support vector machines Tabu search Tumors Ultrasonic imaging Wavelet transforms |
| title | Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions |
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