Feasibility Study of Optical Spectroscopy as a Medical Tool for Diagnosis of Skin Lesions
Skin cancer is one of the most frequently en-countered types of cancer in the Western world. According to the Skin Cancer Foundation Statistics, one in every five Americans develops skin cancer during his/her lifetime. Today, the incurability of advanced cutaneous melanoma raises the importance of i...
Gespeichert in:
| Veröffentlicht in: | International journal of advanced computer science & applications 2016-01, Vol.7 (10) |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 10 |
| container_start_page | |
| container_title | International journal of advanced computer science & applications |
| container_volume | 7 |
| creator | Saf, Asad Ziauddin, Sheikh Horsch, Alexander Ziai, Mahzad Castaneda, Victor Lasser, Tobias Navab, Nassir |
| description | Skin cancer is one of the most frequently en-countered types of cancer in the Western world. According to the Skin Cancer Foundation Statistics, one in every five Americans develops skin cancer during his/her lifetime. Today, the incurability of advanced cutaneous melanoma raises the importance of its early detection. Since the differentiation of early melanoma from other pigmented skin lesions is not a trivial task, even for experienced dermatologists, computer aided diagnosis could become an important tool for reducing the mortality rate of this highly malignant cancer type. In this paper, a computer aided diagnosis system based on machine learning is proposed in order to support the clinical use of optical spectroscopy for skin lesions quantification and classification. The focuses is on a feasibility study of optical spectroscopy as a medical tool for diagnosis. To this end, data acquisition protocols for optical spectroscopy are defined and detailed analysis of feature vectors is performed. Different tech-niques for supervised and unsupervised learning are explored on clinical data, collected from patients with malignant and benign skin lesions. |
| doi_str_mv | 10.14569/IJACSA.2016.071052 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2656507883</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2656507883</sourcerecordid><originalsourceid>FETCH-LOGICAL-c252t-5e538bea2ffb659ae8299c885fc3cbaae144675f56a3bc0914ebf57b4f0df20d3</originalsourceid><addsrcrecordid>eNotkM1OwzAQhC0EEhX0CbhY4pzin6zjHKtCoaiohxQJTpbj2MglxCFOD3l70oa97Eo7s6P9ELqjZEFTEPnD5nW5KpYLRqhYkIwSYBdoxiiIBCAjl-dZJpRkH9doHuOBjMVzJiSfoc-11dGXvvb9gIv-WA04OLxre290jYvWmr4L0YR2wDpijd9sdd7sQ6ixCx1-9PqrCdHHk6_49g3e2uhDE2_RldN1tPP_foPe10_71Uuy3T1vVsttYhiwPgELXJZWM-dKAbm2kuW5kRKc4abU2tI0FRk4EJqXhuQ0taWDrEwdqRwjFb9B99Pdtgu_Rxt7dQjHrhkjFRMggGRS8lHFJ5UZ34mddart_I_uBkWJOmNUE0Z1wqgmjPwPxTZmIg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2656507883</pqid></control><display><type>article</type><title>Feasibility Study of Optical Spectroscopy as a Medical Tool for Diagnosis of Skin Lesions</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Saf, Asad ; Ziauddin, Sheikh ; Horsch, Alexander ; Ziai, Mahzad ; Castaneda, Victor ; Lasser, Tobias ; Navab, Nassir</creator><creatorcontrib>Saf, Asad ; Ziauddin, Sheikh ; Horsch, Alexander ; Ziai, Mahzad ; Castaneda, Victor ; Lasser, Tobias ; Navab, Nassir</creatorcontrib><description>Skin cancer is one of the most frequently en-countered types of cancer in the Western world. According to the Skin Cancer Foundation Statistics, one in every five Americans develops skin cancer during his/her lifetime. Today, the incurability of advanced cutaneous melanoma raises the importance of its early detection. Since the differentiation of early melanoma from other pigmented skin lesions is not a trivial task, even for experienced dermatologists, computer aided diagnosis could become an important tool for reducing the mortality rate of this highly malignant cancer type. In this paper, a computer aided diagnosis system based on machine learning is proposed in order to support the clinical use of optical spectroscopy for skin lesions quantification and classification. The focuses is on a feasibility study of optical spectroscopy as a medical tool for diagnosis. To this end, data acquisition protocols for optical spectroscopy are defined and detailed analysis of feature vectors is performed. Different tech-niques for supervised and unsupervised learning are explored on clinical data, collected from patients with malignant and benign skin lesions.</description><identifier>ISSN: 2158-107X</identifier><identifier>EISSN: 2156-5570</identifier><identifier>DOI: 10.14569/IJACSA.2016.071052</identifier><language>eng</language><publisher>West Yorkshire: Science and Information (SAI) Organization Limited</publisher><subject>Cancer ; Data acquisition ; Diagnosis ; Feasibility studies ; Lesions ; Machine learning ; Melanoma ; Skin cancer ; Spectroscopic analysis ; Spectrum analysis</subject><ispartof>International journal of advanced computer science & applications, 2016-01, Vol.7 (10)</ispartof><rights>2016. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c252t-5e538bea2ffb659ae8299c885fc3cbaae144675f56a3bc0914ebf57b4f0df20d3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Saf, Asad</creatorcontrib><creatorcontrib>Ziauddin, Sheikh</creatorcontrib><creatorcontrib>Horsch, Alexander</creatorcontrib><creatorcontrib>Ziai, Mahzad</creatorcontrib><creatorcontrib>Castaneda, Victor</creatorcontrib><creatorcontrib>Lasser, Tobias</creatorcontrib><creatorcontrib>Navab, Nassir</creatorcontrib><title>Feasibility Study of Optical Spectroscopy as a Medical Tool for Diagnosis of Skin Lesions</title><title>International journal of advanced computer science & applications</title><description>Skin cancer is one of the most frequently en-countered types of cancer in the Western world. According to the Skin Cancer Foundation Statistics, one in every five Americans develops skin cancer during his/her lifetime. Today, the incurability of advanced cutaneous melanoma raises the importance of its early detection. Since the differentiation of early melanoma from other pigmented skin lesions is not a trivial task, even for experienced dermatologists, computer aided diagnosis could become an important tool for reducing the mortality rate of this highly malignant cancer type. In this paper, a computer aided diagnosis system based on machine learning is proposed in order to support the clinical use of optical spectroscopy for skin lesions quantification and classification. The focuses is on a feasibility study of optical spectroscopy as a medical tool for diagnosis. To this end, data acquisition protocols for optical spectroscopy are defined and detailed analysis of feature vectors is performed. Different tech-niques for supervised and unsupervised learning are explored on clinical data, collected from patients with malignant and benign skin lesions.</description><subject>Cancer</subject><subject>Data acquisition</subject><subject>Diagnosis</subject><subject>Feasibility studies</subject><subject>Lesions</subject><subject>Machine learning</subject><subject>Melanoma</subject><subject>Skin cancer</subject><subject>Spectroscopic analysis</subject><subject>Spectrum analysis</subject><issn>2158-107X</issn><issn>2156-5570</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNotkM1OwzAQhC0EEhX0CbhY4pzin6zjHKtCoaiohxQJTpbj2MglxCFOD3l70oa97Eo7s6P9ELqjZEFTEPnD5nW5KpYLRqhYkIwSYBdoxiiIBCAjl-dZJpRkH9doHuOBjMVzJiSfoc-11dGXvvb9gIv-WA04OLxre290jYvWmr4L0YR2wDpijd9sdd7sQ6ixCx1-9PqrCdHHk6_49g3e2uhDE2_RldN1tPP_foPe10_71Uuy3T1vVsttYhiwPgELXJZWM-dKAbm2kuW5kRKc4abU2tI0FRk4EJqXhuQ0taWDrEwdqRwjFb9B99Pdtgu_Rxt7dQjHrhkjFRMggGRS8lHFJ5UZ34mddart_I_uBkWJOmNUE0Z1wqgmjPwPxTZmIg</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Saf, Asad</creator><creator>Ziauddin, Sheikh</creator><creator>Horsch, Alexander</creator><creator>Ziai, Mahzad</creator><creator>Castaneda, Victor</creator><creator>Lasser, Tobias</creator><creator>Navab, Nassir</creator><general>Science and Information (SAI) Organization Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20160101</creationdate><title>Feasibility Study of Optical Spectroscopy as a Medical Tool for Diagnosis of Skin Lesions</title><author>Saf, Asad ; Ziauddin, Sheikh ; Horsch, Alexander ; Ziai, Mahzad ; Castaneda, Victor ; Lasser, Tobias ; Navab, Nassir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c252t-5e538bea2ffb659ae8299c885fc3cbaae144675f56a3bc0914ebf57b4f0df20d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Cancer</topic><topic>Data acquisition</topic><topic>Diagnosis</topic><topic>Feasibility studies</topic><topic>Lesions</topic><topic>Machine learning</topic><topic>Melanoma</topic><topic>Skin cancer</topic><topic>Spectroscopic analysis</topic><topic>Spectrum analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Saf, Asad</creatorcontrib><creatorcontrib>Ziauddin, Sheikh</creatorcontrib><creatorcontrib>Horsch, Alexander</creatorcontrib><creatorcontrib>Ziai, Mahzad</creatorcontrib><creatorcontrib>Castaneda, Victor</creatorcontrib><creatorcontrib>Lasser, Tobias</creatorcontrib><creatorcontrib>Navab, Nassir</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of advanced computer science & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Saf, Asad</au><au>Ziauddin, Sheikh</au><au>Horsch, Alexander</au><au>Ziai, Mahzad</au><au>Castaneda, Victor</au><au>Lasser, Tobias</au><au>Navab, Nassir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Feasibility Study of Optical Spectroscopy as a Medical Tool for Diagnosis of Skin Lesions</atitle><jtitle>International journal of advanced computer science & applications</jtitle><date>2016-01-01</date><risdate>2016</risdate><volume>7</volume><issue>10</issue><issn>2158-107X</issn><eissn>2156-5570</eissn><abstract>Skin cancer is one of the most frequently en-countered types of cancer in the Western world. According to the Skin Cancer Foundation Statistics, one in every five Americans develops skin cancer during his/her lifetime. Today, the incurability of advanced cutaneous melanoma raises the importance of its early detection. Since the differentiation of early melanoma from other pigmented skin lesions is not a trivial task, even for experienced dermatologists, computer aided diagnosis could become an important tool for reducing the mortality rate of this highly malignant cancer type. In this paper, a computer aided diagnosis system based on machine learning is proposed in order to support the clinical use of optical spectroscopy for skin lesions quantification and classification. The focuses is on a feasibility study of optical spectroscopy as a medical tool for diagnosis. To this end, data acquisition protocols for optical spectroscopy are defined and detailed analysis of feature vectors is performed. Different tech-niques for supervised and unsupervised learning are explored on clinical data, collected from patients with malignant and benign skin lesions.</abstract><cop>West Yorkshire</cop><pub>Science and Information (SAI) Organization Limited</pub><doi>10.14569/IJACSA.2016.071052</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2158-107X |
| ispartof | International journal of advanced computer science & applications, 2016-01, Vol.7 (10) |
| issn | 2158-107X 2156-5570 |
| language | eng |
| recordid | cdi_proquest_journals_2656507883 |
| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Cancer Data acquisition Diagnosis Feasibility studies Lesions Machine learning Melanoma Skin cancer Spectroscopic analysis Spectrum analysis |
| title | Feasibility Study of Optical Spectroscopy as a Medical Tool for Diagnosis of Skin Lesions |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T14%3A03%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Feasibility%20Study%20of%20Optical%20Spectroscopy%20as%20a%20Medical%20Tool%20for%20Diagnosis%20of%20Skin%20Lesions&rft.jtitle=International%20journal%20of%20advanced%20computer%20science%20&%20applications&rft.au=Saf,%20Asad&rft.date=2016-01-01&rft.volume=7&rft.issue=10&rft.issn=2158-107X&rft.eissn=2156-5570&rft_id=info:doi/10.14569/IJACSA.2016.071052&rft_dat=%3Cproquest_cross%3E2656507883%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2656507883&rft_id=info:pmid/&rfr_iscdi=true |