Superior skin cancer classification by the combination of human and artificial intelligence.

Superior skin cancer classification by the combination of human and artificial intelligence.

Publication date: Sep 10, 2019

In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification.

Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification).

Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% INTERPRETATION: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems.

Hekler, A., Utikal, J.S., Enk, A.H., Hauschild, A., Weichenthal, M., Maron, R.C., Berking, C., Haferkamp, S., Klode, J., Schadendorf, D., Schilling, B., Holland-Letz, T., Izar, B., von Kalle, C., Fr”ohling, S., Brinker, T.J., and , Collaborators. Superior skin cancer classification by the combination of human and artificial intelligence. 24097. 2019 Eur J Cancer (120):

Concepts Keywords
Artificial Intelligence Artificial intelligence
Benign Machine learning
Binary Computational neuroscience
Binary Classification Articles
Biopsy Emerging technologies
Classifier Artificial neural networks
CNN Statistical classification
Convolutional Neural Networks Classifier
Deep Learning Deep learning
Malignant Melanoma
Melanoma Boosting
Nevi Artificial intelligence

Semantics

Type Source Name
drug DRUGBANK Aspartame
gene UNIPROT KCNK3
drug DRUGBANK Flunarizine
disease MESH diagnoses
gene UNIPROT SET
disease MESH nevi
pathway BSID Melanoma
disease DOID melanoma
disease MESH melanoma
disease DOID skin cancer
disease MESH skin cancer

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