SkinDiRect: Skin Disease Recognition using pattern 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 regarding the features of each disease into a system and enabling it to automate the evaluation process has significantly minimized th...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2007
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/11323 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | 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 regarding the features of each 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 the diagnoses. Developments such as CLARET (Kelm, et al. 2006) in the field of radiology and STARE (Goldbaum, et al. 2000) in the field of ophthalmology have proven the possibility of utilizing such systems in actual practice. Both systems use image representation of diseases as input to generate the corresponding diagnoses. This paper presents a research that extended such technology to the branch of dermatology due to its highly visual nature. In line with its objectives, the study introduced a decision-support system that performs pattern recognition on images to identify the corresponding diseases. The accuracy of the automated diagnoses of the system reached an outstanding 93.16%, thus proving the feasibility and extensibility of the entire research. |
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