SciELO - Scientific Electronic Library Online

 
vol.19 número2Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature ExtractionSemantic Mining based on graph theory and ontologies. Case Study: Cell Signaling Pathways índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Links relacionados

Compartilhar


CLEI Electronic Journal

versão On-line ISSN 0717-5000

Resumo

BAREIRO PANIAGUA, Laura Raquel et al. Computerized Medical Diagnosis of Melanocytic Lesions based on the ABCD approach. CLEIej [online]. 2016, vol.19, n.2, pp.6-6. ISSN 0717-5000.

Melanoma is a type of skin cancer and is caused by the uncontrolled growth of atypical melanocytes. In recent decades, computer aided diagnosis is used to support medical professionals; however, there is still no globally accepted tool. In this context, similar to state-of-the-art we propose a system that receives a dermatoscopy image and provides a diagnostic if the lesion is benign or malignant. This tool is composed with next modules: Preprocessing, Segmentation, Feature Extraction, and Classification. Preprocessing involves the removal of hairs. Segmentation is to isolate the lesion. Feature extraction is considering the ABCD dermoscopy rule. The classification is performed by the Support Vector Machine. Experimental evidence indicates that the proposal has 90.63 % accuracy, 95 % sensitivity, and 83.33 % specificity on a data-set of 104 dermatoscopy images. These results are favorable considering the performance of diagnosis by traditional progress in the area of dermatology

Palavras-chave : Melanoma; Automatic Diagnosis; Image Processing; Machine Learning.

        · resumo em Espanhol     · texto em Inglês     · Inglês ( pdf )

 

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons