Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

Publication date: Aug 13, 2019

Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account.

Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier’s performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories.

Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p

Maron, R.C., Weichenthal, M., Utikal, J.S., Hekler, A., Berking, C., Hauschild, A., Enk, A.H., Haferkamp, S., Klode, J., Schadendorf, D., Jansen, P., Holland-Letz, T., Schilling, B., von Kalle, C., Fr”ohling, S., Gaiser, Hartmann, D., Gesierich, A., K”ahler, K.C., Wehkamp, U., Karoglan, A., B”ar, C., Brinker, T.J., and , Collabrators. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. 23790. 2019 Eur J Cancer (119):

Concepts Keywords
Algorithm Artificial intelligence
Benign Sensitivity and specificity
Binary Classification Dermatology
Biopsy Deep learning
Classifier Convolutional neural network
CNN Cancer
Confidence Interval Melanoma
Convolutional Neural Networks RTT
Deep Learning Articles
Image Classification Fields of mathematics
Melanoma Academic disciplines
Nevi Computational neuroscience
Artificial neural networks
Dermoscopic melanoma nevi
Equal sensitivity algorithm
Convolutional neural networks

Semantics

Type Source Name
drug DRUGBANK Aspartame
disease MESH diagnoses
disease MESH nevi
pathway BSID Melanoma
disease DOID melanoma
disease MESH melanoma
disease DOID skin cancer
disease MESH skin cancer

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