title: Detection of skin cancer by classification of Raman spectra.
abstract: Skin lesion classification based on in vitro Raman spectroscopy is approached using a nonlinear neural network classifier. The classification framework is probabilistic and highly automated. The framework includes a feature extraction for Raman spectra and a fully adaptive and robust feedforward neural network classifier. Moreover, classification rules learned by the neural network may be extracted and evaluated for reproducibility, making it possible to explain the class assignment. The classification performance for the present data set, involving 222 cases and five lesion types, was 80.5%+/-5.3% correct classification of malignant melanoma, which is similar to that of trained dermatologists based on visual inspection. The skin cancer basal cell carcinoma has a classification rate of 95.8%+/-2.7%, which is excellent. The overall classification rate of skin lesions is 94.8%+/-3.0%. Spectral regions, which are important for network classification, are demonstrated to reproduce. Small distinctive bands in the spectrum, corresponding to specific lipids and proteins, are shown to hold the discriminating information which the network uses to diagnose skin lesions.
authors.affiliation: Informatics and Mathematical Modeling, Technical University of Denmark, DK-2800 Kgs Lyngby, Denmark. siggi@imm.dtu.dk
authors.forename: Sigurdur
authors.initials: S
authors.lastname: Sigurdsson
authors.forename: Peter Alshede
authors.initials: PA
authors.lastname: Philipsen
authors.forename: Lars Kai
authors.initials: LK
authors.lastname: Hansen
authors.forename: Jan
authors.initials: J
authors.lastname: Larsen
authors.forename: Monika
authors.initials: M
authors.lastname: Gniadecka
authors.forename: Hans Christian
authors.initials: HC
authors.lastname: Wulf
mesh_terms: D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000145:classification* / Q000175:diagnosis*; D013059:Spectrum Analysis, Raman / Q000379:methods*