Classification of reflectance spectra from pigmented skin lesions, a comparison of multivariate discriminant analysis and artificial neural networks.
Successful treatment of skin cancer, especially melanoma, depends on early detection, but diagnostic accuracy, even by experts, can be as low as 56% so there is an urgent need for a simple, accurate, non-invasive diagnostic tool. In this paper we have compared the performance of an artificial neural network (ANN) and multivariate discriminant analysis (MDA) for the classification of optical reflectance spectra (320 to 1100 nm) from malignant melanoma and benign naevi. The ANN was significantly better than MDA, especially when a larger data set was used, where the classification accuracy was 86.7% for ANN and 72.0% for MDA (p < 0.001). ANN was better at learning new cases than MDA for this particular classification task. This study has confirmed that the convenience of ANNs could lead to the medical community and patients benefiting from the improved diagnostic performance which can be achieved by objective measurement of pigmented skin lesions using spectrophotometry.
Wallace
V P
VP
Department of Physics, Institute of Cancer Research and Royal Marsden NHS Trust, Sutton, Surrey, UK.
Bamber
J C
JC
Crawford
D C
DC
Ott
R J
RJ
Mortimer
P S
PS
D000465:Algorithms; D006801:Humans; D008545:Melanoma / Q000175:diagnosis / Q000000981:diagnostic imaging; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D009506:Nevus / Q000175:diagnosis / Q000000981:diagnostic imaging; D011859:Radiography; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D012984:Software; D013053:Spectrophotometry