Dataset Information

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

ABSTRACT:

Importance

Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.

Objective

To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience.

Design, setting, and participants

A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy.

Main outcomes and measures

The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures.

Results

Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P?Conclusions and relevanceNeural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.

SUBMITTER: Tschandl P

PROVIDER: S-EPMC6439580 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

Tschandl Philipp P Rosendahl Cliff C Akay Bengu Nisa BN Argenziano Giuseppe G Blum Andreas A Braun Ralph P RP Cabo Horacio H Gourhant Jean-Yves JY Kreusch Jürgen J Lallas Aimilios A Lapins Jan J Marghoob Ashfaq A Menzies Scott S Neuber Nina Maria NM Paoli John J Rabinovitz Harold S HS Rinner Christoph C Scope Alon A Soyer H Peter HP Sinz Christoph C Thomas Luc L Zalaudek Iris I Kittler Harald H

JAMA dermatology 20190101 1

<h4>Importance</h4>Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.<h4>Objective</h4>To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience.<h4>Design, setting, and participants</h4>A CNN-based classification model was trained on 7895 dermoscopic and 5829 c ...[more]

PMID: 30484822

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Dataset Information

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

Importance

Objective

Design, setting, and participants

Main outcomes and measures

Results

Publications

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

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