Automatic Classification of Specific Melanocytic Lesions Using Artificial IntelligenceReport as inadecuate

Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence - Download this document for free, or read online. Document in PDF available to download.

BioMed Research International - Volume 2016 2016, Article ID 8934242, 17 pages -

Research ArticleDepartment of Automatics and Biomedical Engineering, AGH University of Science and Technology, Aleja Mickiewicza 30, 30-059 Krakow, Poland

Received 20 November 2015; Revised 23 December 2015; Accepted 24 December 2015

Academic Editor: Yudong Cai

Copyright © 2016 Joanna Jaworek-Korjakowska and Paweł Kłeczek. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis CAD systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz-Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

Author: Joanna Jaworek-Korjakowska and Paweł Kłeczek



Related documents