SkinDiRecT: Skin disease recognition
Research in the field of decision-support in medicine has paved the way to the advent of new approaches to medical diagnoses. This concept of storing prior knowledge of the features of a disease into a system and enabling it to automate the evaluation process has significantly minimized the amount o...
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2007
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oai:animorepository.dlsu.edu.ph:faculty_research-144882024-05-28T08:10:15Z SkinDiRecT: Skin disease recognition Lim, Lance Edward T. Sarmiento, Eliel Jan O. Sy, Billy Andrew G. UySison, Lou Allen M. Ap-apid, Rigan P. Research in the field of decision-support in medicine has paved the way to the advent of new approaches to medical diagnoses. This concept of storing prior knowledge of the features of a disease into a system and enabling it to automate the evaluation process has significantly minimized the amount of time required to diagnose certain disorders and noticeably improved the accuracy of diagnoses. This paper presents a system, which uses image pattern recognition techniques to identify or recognize skin diseases from image inputs. The accuracy of the automated diagnosis reached a high of 93.16%. 2007-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/12586 Faculty Research Work Animo Repository Pattern recognition systems Skin--Diseases Computer Sciences |
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De La Salle University |
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De La Salle University Library |
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Asia |
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Philippines Philippines |
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De La Salle University Library |
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DLSU Institutional Repository |
| topic |
Pattern recognition systems Skin--Diseases Computer Sciences |
| spellingShingle |
Pattern recognition systems Skin--Diseases Computer Sciences Lim, Lance Edward T. Sarmiento, Eliel Jan O. Sy, Billy Andrew G. UySison, Lou Allen M. Ap-apid, Rigan P. SkinDiRecT: Skin disease recognition |
| description |
Research in the field of decision-support in medicine has paved the way to the advent of new approaches to medical diagnoses. This concept of storing prior knowledge of the features of a disease into a system and enabling it to automate the evaluation process has significantly minimized the amount of time required to diagnose certain disorders and noticeably improved the accuracy of diagnoses. This paper presents a system, which uses image pattern recognition techniques to identify or recognize skin diseases from image inputs. The accuracy of the automated diagnosis reached a high of 93.16%. |
| format |
text |
| author |
Lim, Lance Edward T. Sarmiento, Eliel Jan O. Sy, Billy Andrew G. UySison, Lou Allen M. Ap-apid, Rigan P. |
| author_facet |
Lim, Lance Edward T. Sarmiento, Eliel Jan O. Sy, Billy Andrew G. UySison, Lou Allen M. Ap-apid, Rigan P. |
| author_sort |
Lim, Lance Edward T. |
| title |
SkinDiRecT: Skin disease recognition |
| title_short |
SkinDiRecT: Skin disease recognition |
| title_full |
SkinDiRecT: Skin disease recognition |
| title_fullStr |
SkinDiRecT: Skin disease recognition |
| title_full_unstemmed |
SkinDiRecT: Skin disease recognition |
| title_sort |
skindirect: skin disease recognition |
| publisher |
Animo Repository |
| publishDate |
2007 |
| url |
https://animorepository.dlsu.edu.ph/faculty_research/12586 |
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1802997444363943936 |