AN AUTOMATED SKIN DISEASE DIAGNOSTIC SYSTEM BASED ON DEEP LEARNING MODEL
The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in s...
Gespeichert in:
| Veröffentlicht in: | Journal of engineering studies and research 2021-07, Vol.27 (3), p.43-50 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 50 |
|---|---|
| container_issue | 3 |
| container_start_page | 43 |
| container_title | Journal of engineering studies and research |
| container_volume | 27 |
| creator | Omotosho, Lawrence Sotonwa, Kehinde Adegoke, Benjamin Oyeniran, Oluwashina Oyeniyi, Joshua |
| description | The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in saving numerous patients and are constantly improving our quality of life. Technology in the medical sector has also had a major effect on almost all healthcare professional techniques and practices. In order to facilitate rapid diagnosis and treatment of different skin diseases by the use of a deep learning model, this study developed a comprehensive framework to improve the decision-making of dermatologists in Nigeria in terms of the diagnosis of selected skin diseases. The developed system achieved the network accuracy of 98.44 % and the validation accuracy of the test set is 99.44 % as specified by the training results, further testing reveal that the developed system yielded rejection rate of 2.2 % and recognition accuracy of 97.8 %. |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2618171213</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2618171213</sourcerecordid><originalsourceid>FETCH-proquest_journals_26181712133</originalsourceid><addsrcrecordid>eNqNitEKgjAYRkcUJOU7DLoWnHM6L5f7M0m3aOuiKykwKCJL8_3bRQ_Q1fn4zpkgL6JxHMQZjaZuhwkPUsayOfKH4XYJWZhSyjj30FYoLI5W18KCxGZXKixLA8KAoyiUNrbMsTkZCzVeu1ti7RKAPa5AHFSpClxrCdUSza7nx9D6Py7QagM23wavvnuP7fBp7t3YP51qooRwkpKIUPpf9QUamjZ8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2618171213</pqid></control><display><type>article</type><title>AN AUTOMATED SKIN DISEASE DIAGNOSTIC SYSTEM BASED ON DEEP LEARNING MODEL</title><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Omotosho, Lawrence ; Sotonwa, Kehinde ; Adegoke, Benjamin ; Oyeniran, Oluwashina ; Oyeniyi, Joshua</creator><creatorcontrib>Omotosho, Lawrence ; Sotonwa, Kehinde ; Adegoke, Benjamin ; Oyeniran, Oluwashina ; Oyeniyi, Joshua</creatorcontrib><description>The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in saving numerous patients and are constantly improving our quality of life. Technology in the medical sector has also had a major effect on almost all healthcare professional techniques and practices. In order to facilitate rapid diagnosis and treatment of different skin diseases by the use of a deep learning model, this study developed a comprehensive framework to improve the decision-making of dermatologists in Nigeria in terms of the diagnosis of selected skin diseases. The developed system achieved the network accuracy of 98.44 % and the validation accuracy of the test set is 99.44 % as specified by the training results, further testing reveal that the developed system yielded rejection rate of 2.2 % and recognition accuracy of 97.8 %.</description><identifier>ISSN: 2068-7559</identifier><identifier>EISSN: 2344-4932</identifier><language>eng</language><publisher>Bacau: Universitatea "Vasile Alecsandri" din Bacau</publisher><subject>Accuracy ; Artificial intelligence ; Bacterial infections ; Classification ; Cysts ; Decision making ; Deep learning ; Dermatitis ; Dermatology ; Diagnostic systems ; Hair ; Health care ; Infections ; Infectious diseases ; Medical diagnosis ; Neural networks ; Rejection rate ; Skin cancer ; Skin diseases ; Viral infections</subject><ispartof>Journal of engineering studies and research, 2021-07, Vol.27 (3), p.43-50</ispartof><rights>Copyright Universitatea "Vasile Alecsandri" din Bacau 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>Omotosho, Lawrence</creatorcontrib><creatorcontrib>Sotonwa, Kehinde</creatorcontrib><creatorcontrib>Adegoke, Benjamin</creatorcontrib><creatorcontrib>Oyeniran, Oluwashina</creatorcontrib><creatorcontrib>Oyeniyi, Joshua</creatorcontrib><title>AN AUTOMATED SKIN DISEASE DIAGNOSTIC SYSTEM BASED ON DEEP LEARNING MODEL</title><title>Journal of engineering studies and research</title><description>The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in saving numerous patients and are constantly improving our quality of life. Technology in the medical sector has also had a major effect on almost all healthcare professional techniques and practices. In order to facilitate rapid diagnosis and treatment of different skin diseases by the use of a deep learning model, this study developed a comprehensive framework to improve the decision-making of dermatologists in Nigeria in terms of the diagnosis of selected skin diseases. The developed system achieved the network accuracy of 98.44 % and the validation accuracy of the test set is 99.44 % as specified by the training results, further testing reveal that the developed system yielded rejection rate of 2.2 % and recognition accuracy of 97.8 %.</description><subject>Accuracy</subject><subject>Artificial intelligence</subject><subject>Bacterial infections</subject><subject>Classification</subject><subject>Cysts</subject><subject>Decision making</subject><subject>Deep learning</subject><subject>Dermatitis</subject><subject>Dermatology</subject><subject>Diagnostic systems</subject><subject>Hair</subject><subject>Health care</subject><subject>Infections</subject><subject>Infectious diseases</subject><subject>Medical diagnosis</subject><subject>Neural networks</subject><subject>Rejection rate</subject><subject>Skin cancer</subject><subject>Skin diseases</subject><subject>Viral infections</subject><issn>2068-7559</issn><issn>2344-4932</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNitEKgjAYRkcUJOU7DLoWnHM6L5f7M0m3aOuiKykwKCJL8_3bRQ_Q1fn4zpkgL6JxHMQZjaZuhwkPUsayOfKH4XYJWZhSyjj30FYoLI5W18KCxGZXKixLA8KAoyiUNrbMsTkZCzVeu1ti7RKAPa5AHFSpClxrCdUSza7nx9D6Py7QagM23wavvnuP7fBp7t3YP51qooRwkpKIUPpf9QUamjZ8</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Omotosho, Lawrence</creator><creator>Sotonwa, Kehinde</creator><creator>Adegoke, Benjamin</creator><creator>Oyeniran, Oluwashina</creator><creator>Oyeniyi, Joshua</creator><general>Universitatea "Vasile Alecsandri" din Bacau</general><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BYOGL</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20210701</creationdate><title>AN AUTOMATED SKIN DISEASE DIAGNOSTIC SYSTEM BASED ON DEEP LEARNING MODEL</title><author>Omotosho, Lawrence ; Sotonwa, Kehinde ; Adegoke, Benjamin ; Oyeniran, Oluwashina ; Oyeniyi, Joshua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_26181712133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Artificial intelligence</topic><topic>Bacterial infections</topic><topic>Classification</topic><topic>Cysts</topic><topic>Decision making</topic><topic>Deep learning</topic><topic>Dermatitis</topic><topic>Dermatology</topic><topic>Diagnostic systems</topic><topic>Hair</topic><topic>Health care</topic><topic>Infections</topic><topic>Infectious diseases</topic><topic>Medical diagnosis</topic><topic>Neural networks</topic><topic>Rejection rate</topic><topic>Skin cancer</topic><topic>Skin diseases</topic><topic>Viral infections</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Omotosho, Lawrence</creatorcontrib><creatorcontrib>Sotonwa, Kehinde</creatorcontrib><creatorcontrib>Adegoke, Benjamin</creatorcontrib><creatorcontrib>Oyeniran, Oluwashina</creatorcontrib><creatorcontrib>Oyeniyi, Joshua</creatorcontrib><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>East Europe, Central Europe Database</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Engineering 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>Engineering Collection</collection><jtitle>Journal of engineering studies and research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Omotosho, Lawrence</au><au>Sotonwa, Kehinde</au><au>Adegoke, Benjamin</au><au>Oyeniran, Oluwashina</au><au>Oyeniyi, Joshua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AN AUTOMATED SKIN DISEASE DIAGNOSTIC SYSTEM BASED ON DEEP LEARNING MODEL</atitle><jtitle>Journal of engineering studies and research</jtitle><date>2021-07-01</date><risdate>2021</risdate><volume>27</volume><issue>3</issue><spage>43</spage><epage>50</epage><pages>43-50</pages><issn>2068-7559</issn><eissn>2344-4932</eissn><abstract>The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in saving numerous patients and are constantly improving our quality of life. Technology in the medical sector has also had a major effect on almost all healthcare professional techniques and practices. In order to facilitate rapid diagnosis and treatment of different skin diseases by the use of a deep learning model, this study developed a comprehensive framework to improve the decision-making of dermatologists in Nigeria in terms of the diagnosis of selected skin diseases. The developed system achieved the network accuracy of 98.44 % and the validation accuracy of the test set is 99.44 % as specified by the training results, further testing reveal that the developed system yielded rejection rate of 2.2 % and recognition accuracy of 97.8 %.</abstract><cop>Bacau</cop><pub>Universitatea "Vasile Alecsandri" din Bacau</pub></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2068-7559 |
| ispartof | Journal of engineering studies and research, 2021-07, Vol.27 (3), p.43-50 |
| issn | 2068-7559 2344-4932 |
| language | eng |
| recordid | cdi_proquest_journals_2618171213 |
| source | DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Accuracy Artificial intelligence Bacterial infections Classification Cysts Decision making Deep learning Dermatitis Dermatology Diagnostic systems Hair Health care Infections Infectious diseases Medical diagnosis Neural networks Rejection rate Skin cancer Skin diseases Viral infections |
| title | AN AUTOMATED SKIN DISEASE DIAGNOSTIC SYSTEM BASED ON DEEP LEARNING MODEL |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T06%3A16%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=AN%20AUTOMATED%20SKIN%20DISEASE%20DIAGNOSTIC%20SYSTEM%20BASED%20ON%20DEEP%20LEARNING%20MODEL&rft.jtitle=Journal%20of%20engineering%20studies%20and%20research&rft.au=Omotosho,%20Lawrence&rft.date=2021-07-01&rft.volume=27&rft.issue=3&rft.spage=43&rft.epage=50&rft.pages=43-50&rft.issn=2068-7559&rft.eissn=2344-4932&rft_id=info:doi/&rft_dat=%3Cproquest%3E2618171213%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2618171213&rft_id=info:pmid/&rfr_iscdi=true |