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Automated Detection of Benign and Malignant in Breast Histopathology Images
Breast cancer detection and classification using histological images play a critical role in the breast cancer diagnosis process. This paper presents a framework for autodetection and classification of breast cancer from microscopic histological images. The images are classified into benign or malig...
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| creator | Baker, Qanita Bani Zaitoun, Toqa' Abu Banat, Sajda Eaydat, Eman Alsmirat, Mohammad |
| description | Breast cancer detection and classification using histological images play a critical role in the breast cancer diagnosis process. This paper presents a framework for autodetection and classification of breast cancer from microscopic histological images. The images are classified into benign or malignant. The proposed framework involves several steps which include image enhancement, image segmentation, features extraction, and images classification. The proposed framework utilizes a novel combination of K-means clustering and watershed algorithms in the segmentation step. We used K-means clustering to produce an initial segmented image and then we applied the watershed segmentation algorithm. Classification results show that the proposed method effectively detect and classify breast cancer from histological image with accuracy of 70.7% using a proposed Rule-Based classifier and 86.5% using a Decision Tree classifier. |
| doi_str_mv | 10.1109/AICCSA.2018.8612799 |
| format | conference_proceeding |
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Classification results show that the proposed method effectively detect and classify breast cancer from histological image with accuracy of 70.7% using a proposed Rule-Based classifier and 86.5% using a Decision Tree classifier.</description><subject>Benign</subject><subject>Breast cancer</subject><subject>Clustering algorithms</subject><subject>Digital Pathology</subject><subject>Feature extraction</subject><subject>Image Analysis</subject><subject>Image color analysis</subject><subject>Image segmentation</subject><subject>K-means</subject><subject>Malignant</subject><subject>Microscopic Images</subject><subject>Watershed</subject><issn>2161-5330</issn><isbn>1538691205</isbn><isbn>9781538691205</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2018</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj8tOwzAQAA0SEm3hC3rxD6TYXtvJHtPwaEQRB-BcLfEmGLVJlZhD_x4kepo5jTRCLLVaaa3wrqyr6q1cGaWLVeG1yREvxFw7KDxqo9ylmBntdeYA1LWYT9O3UoCmcDPxXP6k4UCJg7znxE2KQy-HVq65j10vqQ_yhfZ_Sn2SsZfrkWlKchOnNBwpfQ37oTvJ-kAdTzfiqqX9xLdnLsTH48N7tcm2r091VW6zqHOXMsAmtzmzJY8IvrVNY4wCXTAFCBacbbFoOFfaU7AIrskJrLKfiMEH9LAQy_9uZObdcYwHGk-78zj8AjYmTLY</recordid><startdate>201810</startdate><enddate>201810</enddate><creator>Baker, Qanita Bani</creator><creator>Zaitoun, Toqa' Abu</creator><creator>Banat, Sajda</creator><creator>Eaydat, Eman</creator><creator>Alsmirat, Mohammad</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201810</creationdate><title>Automated Detection of Benign and Malignant in Breast Histopathology Images</title><author>Baker, Qanita Bani ; Zaitoun, Toqa' Abu ; Banat, Sajda ; Eaydat, Eman ; Alsmirat, Mohammad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-39c747ee4a69936f4cc220318ead3d4354f98ce7016ad4935c7a3404b99d6d963</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Benign</topic><topic>Breast cancer</topic><topic>Clustering algorithms</topic><topic>Digital Pathology</topic><topic>Feature extraction</topic><topic>Image Analysis</topic><topic>Image color analysis</topic><topic>Image segmentation</topic><topic>K-means</topic><topic>Malignant</topic><topic>Microscopic Images</topic><topic>Watershed</topic><toplevel>online_resources</toplevel><creatorcontrib>Baker, Qanita Bani</creatorcontrib><creatorcontrib>Zaitoun, Toqa' Abu</creatorcontrib><creatorcontrib>Banat, Sajda</creatorcontrib><creatorcontrib>Eaydat, Eman</creatorcontrib><creatorcontrib>Alsmirat, Mohammad</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Baker, Qanita Bani</au><au>Zaitoun, Toqa' Abu</au><au>Banat, Sajda</au><au>Eaydat, Eman</au><au>Alsmirat, Mohammad</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automated Detection of Benign and Malignant in Breast Histopathology Images</atitle><btitle>2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)</btitle><stitle>AICCSA</stitle><date>2018-10</date><risdate>2018</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><eissn>2161-5330</eissn><eisbn>1538691205</eisbn><eisbn>9781538691205</eisbn><abstract>Breast cancer detection and classification using histological images play a critical role in the breast cancer diagnosis process. This paper presents a framework for autodetection and classification of breast cancer from microscopic histological images. The images are classified into benign or malignant. The proposed framework involves several steps which include image enhancement, image segmentation, features extraction, and images classification. The proposed framework utilizes a novel combination of K-means clustering and watershed algorithms in the segmentation step. We used K-means clustering to produce an initial segmented image and then we applied the watershed segmentation algorithm. Classification results show that the proposed method effectively detect and classify breast cancer from histological image with accuracy of 70.7% using a proposed Rule-Based classifier and 86.5% using a Decision Tree classifier.</abstract><pub>IEEE</pub><doi>10.1109/AICCSA.2018.8612799</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2161-5330 |
| ispartof | 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), 2018, p.1-5 |
| issn | 2161-5330 |
| language | eng |
| recordid | cdi_ieee_primary_8612799 |
| source | IEEE Xplore All Conference Series |
| subjects | Benign Breast cancer Clustering algorithms Digital Pathology Feature extraction Image Analysis Image color analysis Image segmentation K-means Malignant Microscopic Images Watershed |
| title | Automated Detection of Benign and Malignant in Breast Histopathology Images |
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