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Characterization of digital medical images utilizing support vector machines
- Source :
- BMC Medical Informatics and Decision Making, Vol 4, Iss 1, p 4 (2004), BMC Medical Informatics and Decision Making
- Publisher :
- Springer Nature
-
Abstract
- Background In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using Support Vector Machines and present the results of a preliminary study. Methods The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared. Results The SVM (Support Vector Machines) algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi), while the neural networks performed approximately the same. Conclusion The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis.
- Subjects :
- Skin Neoplasms
Computer science
Data classification
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
Health Informatics
lcsh:Computer applications to medicine. Medical informatics
Image (mathematics)
Digital image
Image Interpretation, Computer-Assisted
Humans
Computer vision
Melanoma
Artificial neural network
business.industry
Health Policy
Pattern recognition
Linear discriminant analysis
Computer Science Applications
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Classification methods
lcsh:R858-859.7
Neural Networks, Computer
Artificial intelligence
business
Dysplastic Nevus Syndrome
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 14726947
- Volume :
- 4
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Medical Informatics and Decision Making
- Accession number :
- edsair.doi.dedup.....73fdc580e9cdd8fea7de99f2b50b5299
- Full Text :
- https://doi.org/10.1186/1472-6947-4-4