Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification
In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing pr...
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| creator | Ho Tak Lau Al-Jumaily, A. |
| description | In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo. |
| doi_str_mv | 10.1109/SoCPaR.2009.80 |
| format | Conference Proceeding |
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The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo.</description><identifier>ISBN: 1424453305</identifier><identifier>ISBN: 9781424453306</identifier><identifier>EISBN: 0769538797</identifier><identifier>EISBN: 9780769538792</identifier><identifier>DOI: 10.1109/SoCPaR.2009.80</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cancer detection ; classification ; computer based detection ; neural network ; Skin cancer</subject><ispartof>2009 International Conference of Soft Computing and Pattern Recognition, 2009, p.375-380</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5370977$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5370977$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ho Tak Lau</creatorcontrib><creatorcontrib>Al-Jumaily, A.</creatorcontrib><title>Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification</title><title>2009 International Conference of Soft Computing and Pattern Recognition</title><addtitle>SOCPAR</addtitle><description>In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo.</description><subject>Cancer detection</subject><subject>classification</subject><subject>computer based detection</subject><subject>neural network</subject><subject>Skin cancer</subject><isbn>1424453305</isbn><isbn>9781424453306</isbn><isbn>0769538797</isbn><isbn>9780769538792</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotzL1OwzAUBWAjhAQtXVlY_AIJ13bsa7OVUH6kqiAKA1N1kziS1bRBiSOUtycIlnOGo_MxdiUgFQLczbbNX-ktlQAutXDCZoDGaWXR4SmbiUxmmVYK9Dlb9H0oQBo0NpPugn0uh9geKIaSmmbkK-qmvPfRlzG0R97WfLsPR57TsfTdLd_GoRr5HfW-4tO8GXxHDd_4-N3ued7QpNcT9fu9ZGc1Nb1f_PecfTys3vOnZP3y-Jwv10mQQseELFUaCdCBL0C7onK2UJkuHcqiFGSRDDirQVde1ZUBJRxagbWRQipDas6u_9zgvd99deFA3bjTCsEhqh-VElHO</recordid><startdate>20090101</startdate><enddate>20090101</enddate><creator>Ho Tak Lau</creator><creator>Al-Jumaily, A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20090101</creationdate><title>Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification</title><author>Ho Tak Lau ; Al-Jumaily, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i215t-a8ad57a0790eb059bd98b345c972bc1a87a6098505de3fd603197817f621236a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Cancer detection</topic><topic>classification</topic><topic>computer based detection</topic><topic>neural network</topic><topic>Skin cancer</topic><toplevel>online_resources</toplevel><creatorcontrib>Ho Tak Lau</creatorcontrib><creatorcontrib>Al-Jumaily, A.</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 Electronic Library (IEL)</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>Ho Tak Lau</au><au>Al-Jumaily, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification</atitle><btitle>2009 International Conference of Soft Computing and Pattern Recognition</btitle><stitle>SOCPAR</stitle><date>2009-01-01</date><risdate>2009</risdate><spage>375</spage><epage>380</epage><pages>375-380</pages><isbn>1424453305</isbn><isbn>9781424453306</isbn><eisbn>0769538797</eisbn><eisbn>9780769538792</eisbn><abstract>In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo.</abstract><pub>IEEE</pub><doi>10.1109/SoCPaR.2009.80</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Cancer detection classification computer based detection neural network Skin cancer |
| title | Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification |
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