Malignant melanoma detection by Bag-of-Features classification.
In this paper, we apply a Bag-of-Features approach to malignant melanoma detection based on epiluminescence microscopy imaging. Each skin lesion is represented by a histogram of codewords or clusters identified from a training data set. Classification results using Naive Bayes classification and Support Vector Machines are reported. The best performance obtained is 82.21% on a dataset of 100 skin lesion images. Furthermore, since in melanoma screening false negative errors have a much higher impact and associated cost than false positive ones, we use the Neyman-Pearson score in our model selection scheme.
Situ
Ning
N
Computer Science at the University of Houston, USA.
Yuan
Xiaojing
X
Chen
Ji
J
Zouridakis
George
G
D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D005189:False Positive Reactions; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D008545:Melanoma / Q000175:diagnosis* / Q000473:pathology; D015233:Models, Statistical; D009506:Nevus / Q000175:diagnosis / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000473:pathology