Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images
Abstract The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ su...
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Veröffentlicht in: | Logic journal of the IGPL 2022-07, Vol.30 (4), p.649-663 |
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creator | Simić, Svetlana Simić, Svetislav D Banković, Zorana Ivkov-Simić, Milana Villar, José R Simić, Dragan |
description | Abstract
The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches. |
doi_str_mv | 10.1093/jigpal/jzab009 |
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The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches.</description><identifier>ISSN: 1367-0751</identifier><identifier>EISSN: 1368-9894</identifier><identifier>DOI: 10.1093/jigpal/jzab009</identifier><language>eng</language><publisher>Oxford University Press</publisher><ispartof>Logic journal of the IGPL, 2022-07, Vol.30 (4), p.649-663</ispartof><rights>The Author(s) 2021. Published by Oxford University Press. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c243t-5e7a6066cebf8366e390400faef597211cb270399fb7951ca124cb2d52f4ef763</citedby><cites>FETCH-LOGICAL-c243t-5e7a6066cebf8366e390400faef597211cb270399fb7951ca124cb2d52f4ef763</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Simić, Svetlana</creatorcontrib><creatorcontrib>Simić, Svetislav D</creatorcontrib><creatorcontrib>Banković, Zorana</creatorcontrib><creatorcontrib>Ivkov-Simić, Milana</creatorcontrib><creatorcontrib>Villar, José R</creatorcontrib><creatorcontrib>Simić, Dragan</creatorcontrib><title>Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images</title><title>Logic journal of the IGPL</title><description>Abstract
The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches.</description><issn>1367-0751</issn><issn>1368-9894</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNqFkD1PwzAYhC0EEqWwMntlSPvaTux4rMJXpQoG2jlyjF05TeLITkDw6-nXznSn090ND0L3BGYEJJvXbturZl7_qgpAXqAJYTxPZC7Ty6MXCYiMXKObGGsASHOaTdDm0ZgeF7778s04ON-pBr-ZMRxl-PZhF7Hv8GIcfKsGp3HRqBiddVod2tj6gD92rsPrsfVjwMtWbU28RVdWNdHcnXWKNs9P6-I1Wb2_LIvFKtE0ZUOSGaE4cK5NZXPGuWESUgCrjM2koIToigpgUtpKyIxoRWi6jz4zalNjBWdTNDv96uBjDMaWfXCtCj8lgfIApTxBKc9Q9oOH08CP_X_dP4VPZw8</recordid><startdate>20220725</startdate><enddate>20220725</enddate><creator>Simić, Svetlana</creator><creator>Simić, Svetislav D</creator><creator>Banković, Zorana</creator><creator>Ivkov-Simić, Milana</creator><creator>Villar, José R</creator><creator>Simić, Dragan</creator><general>Oxford University Press</general><scope>TOX</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20220725</creationdate><title>Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images</title><author>Simić, Svetlana ; Simić, Svetislav D ; Banković, Zorana ; Ivkov-Simić, Milana ; Villar, José R ; Simić, Dragan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c243t-5e7a6066cebf8366e390400faef597211cb270399fb7951ca124cb2d52f4ef763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Simić, Svetlana</creatorcontrib><creatorcontrib>Simić, Svetislav D</creatorcontrib><creatorcontrib>Banković, Zorana</creatorcontrib><creatorcontrib>Ivkov-Simić, Milana</creatorcontrib><creatorcontrib>Villar, José R</creatorcontrib><creatorcontrib>Simić, Dragan</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>CrossRef</collection><jtitle>Logic journal of the IGPL</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Simić, Svetlana</au><au>Simić, Svetislav D</au><au>Banković, Zorana</au><au>Ivkov-Simić, Milana</au><au>Villar, José R</au><au>Simić, Dragan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images</atitle><jtitle>Logic journal of the IGPL</jtitle><date>2022-07-25</date><risdate>2022</risdate><volume>30</volume><issue>4</issue><spage>649</spage><epage>663</epage><pages>649-663</pages><issn>1367-0751</issn><eissn>1368-9894</eissn><abstract>Abstract
The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches.</abstract><pub>Oxford University Press</pub><doi>10.1093/jigpal/jzab009</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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title | Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images |
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