Title :
Inductive learning of skin lesion images for early diagnosis of melanoma
Author :
Surówka, Grzegorz
Author_Institution :
Fac. of Phys., Astron. & Appl. Comput. Sci., Jagiellonian Univ., Krakow
Abstract :
We take advantage of natural induction methods to build classifiers of the pigmented skin lesion images. This methodology can be treated as a non-invasive approach to early diagnosis of melanoma. We use the AQ21 application, which is based on the attributional calculus, to discover patterns in the skin images. Our classifier has good efficiency and may potentially be an important diagnostic aid.
Keywords :
calculus; cancer; feature extraction; image classification; learning by example; medical image processing; skin; tumours; wavelet transforms; attributional calculus; epidemiology; feature selection; image classification; inductive learning; malignant human cancer; melanoma early diagnosis; natural induction methods; pattern discovery; pigmented skin lesion image; wavelet transform; Calculus; Cancer; Learning systems; Lesions; Machine learning; Malignant tumors; Pigmentation; Probes; Skin; Wavelet analysis;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634165
