Performance of AI Methods in Detecting Melanoma

Arve Kjoelen
M. J. Thompson
Scott E. Umbaugh
Randy Hays Moss, Missouri University of Science and Technology
William V. Stoecker, Missouri University of Science and Technology

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This research has shown that features extracted from color skin tumor images by computer vision methods can be reliable discriminators of malignant tumors from benign ones. Reliability was demonstrated by the monotonically increasing success ratios with increasing training set size and by the small standard deviations from the mean success rates. An average success rate of 70 percent in diagnosing melanoma was attained for a training set size of 60 percent. The presence or absence of atypical moles in the training and test sets was shown to have a dramatic impact on the effectiveness of the generated classification rules. This was the case with both AIM and lst-Class, and indicates a high potential for success if a method can be found for discriminating between atypical moles and melanoma