C4.5: programs for machine learning
C4.5: programs for machine learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Introducing automated melanoma detection in a topic map based image retrieval system
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Reviewing State of the Art AI Systems for Skin Cancer Diagnosis
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Overview of advanced computer vision systems for skin lesions characterization
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Neural networks and other machine learning methods in cancer research
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A comparative study on computerised diagnostic performance of hepatitis disease using ANNs
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Identifying user preferences with Wrapper-based Decision Trees
Expert Systems with Applications: An International Journal
A multilevel information fusion approach for visual quality inspection
Information Fusion
ROC analysis as a useful tool for performance evaluation of artificial neural networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Methodological review: Computerized analysis of pigmented skin lesions: A review
Artificial Intelligence in Medicine
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Melanoma is the most dangerous skin cancer and early diagnosis is the key factor in its successful treatment. Well-trained dermatologists reach a diagnosis via visual inspection, and reach sensitivity and specificity levels of about 80%. Several computerised diagnostic systems were reported in the literature using different classification algorithms. In this paper, we will illustrate a novel approach by which a suitable combination of different classifiers is used in order to improve the diagnostic performances of single classifiers. We used three different kinds of classifiers, namely linear discriminant analysis (LDA), k-nearest neighbour (k-NN) and a decision tree, the inputs of which are 38 geometric and colorimetric features automatically extracted from digital images of skin lesions. Multiple classifiers were generated by combining the diagnostic outputs of single classifiers with appropriate voting schemata. This approach was evaluated on a set of 152 digital skin images. We compared the performances of multiple classifiers (2- and 3-classifier groups) between them and with respect to single ones (1-classifier group). We further compared the classifiers' performances with those of eight dermatologists. Classifiers' performances were measured in terms of distance from the ideal classifier. Compared with 1- and 2-classifier groups, performances of 3-classifier systems were significantly higher (P