Classifying skin lesion images into primary morphologies
Date of Publication
2016
Document Type
Master's Thesis
Degree Name
Master of Science in Computer Science
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Conrado D. Ruiz, Jr.
Defense Panel Chair
Arnulfo P. Azcarraga
Defense Panel Member
Conrado D. Ruiz, Jr.
Arnulfo P. Azcarraga
Joel P. Ilao
Abstract/Summary
Skin lesions are widely common irregularities in skin. Most of the research done by computer scientists on processing images of skin lesions focus on skin cancer malignancy. Less research focus has been on classifying skin lesions into their corresponding skin diseases. The classification of skin lesions into skin diseases is di cult given the large number of skin diseases that exist. It may be suitable to rest classify skin lesions by more general categories to reduce complexity. One such general categorization scheme is through the morphology of skin lesions. Morphology can serve as a viable means of categorizing skin lesions as it is descriptive of a skin lesions structure and appearance. Thus, this research aims to model a system that classifies skin lesions into the primary morphologies in dermatological nomenclature. This was accomplished by applying methods in skin malignancy and skin disease research into the problem of classification by morphology.
Based on the results, further research is needed to have a deeper analysis of classification by morphology, especially as the research is exploratory. Feature Selection provided no significant increase, and although dropping color channel features provided a boost in performance, certain color channel features may need to be opted in. For this research, Multilayer Perceptron provided the best output based on Cohen's Kappa, falling at 0.413 and 0.436 for the 4 class test and 3 class test, respectively.
Abstract Format
html
Language
English
Format
Electronic
Accession Number
CDTG006831
Shelf Location
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
Physical Description
1 computer optical disc ; 4 3/4 in.
Recommended Citation
Macatangay, J. A. (2016). Classifying skin lesion images into primary morphologies. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/5271