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Dermatologist-level classification of skin cancer

An artificial intelligence trained to classify images of skin lesions as benign lesions or malignant skin cancers achieves the accuracy of board-certified dermatologists.

In this work, we pretrain a deep neural network at general object recognition, then fine-tune it on a dataset of ~130,000 skin lesion images comprised of over 2000 diseases.

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Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions.

Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets — consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: malignant carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer.

The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 and can therefore potentially provide low-cost universal access to vital diagnostic care.

Figure 1

Our classification technique is a deep CNN. Data flow is from left to right: an image of a skin lesion (for example, melanoma) is sequentially warped into a probability distribution over clinical classes of skin disease using a deep neural network trained on our dataset. Inception v3 CNN architecture reprinted from

Figure 3

Skin cancer classification performance of the CNN and dermatologists. a, The deep learning CNN outperforms the average of the dermatologists at skin cancer classification (keratinocyte carcinomas and melanomas) using photographic and dermoscopic images. For each test, previously unseen, biopsy-proven images of lesions are displayed, and dermatologists are asked if they would: biopsy/treat the lesion or reassure the patient. A dermatologist outputs a single prediction per image and is thus represented by a single red point. The green points are the average of the dermatologists for each task, with error bars denoting one standard deviation (calculated from n = 25, 22 and 21 tested dermatologists for carcinoma, melanoma and melanoma under dermoscopy, respectively). The CNN is represented by the blue curve, and the AUC is the CNN’s measure of performance, with a maximum value of 1. The CNN achieves superior performance to a dermatologist if the sensitivity–specificity point of the dermatologist lies below the blue curve, which most do. b, The deep learning CNN exhibits reliable cancer classification when tested on a larger dataset. We tested the CNN on more images to demonstrate robust and reliable cancer classification. The CNN’s curves are smoother owing to the larger test set.


  • Andre Esteva
    Andre Esteva
    PhD Candidate
    Electrical Engineering

    Stanford University Artificial Intelligence Lab

  • Brett Kuprel
    Brett Kuprel
    PhD Candidate
    Electrical Engineering

    Stanford University Artificial Intelligence Lab

  • Rob Novoa
    Rob Novoa
    Clinical Assistant Professor

    Department of Dermatology, Department of Pathology, Stanford University

  • Justin Ko
    Justin Ko
    Clinical Associate Professor

    Department of Dermatology, Stanford University

  • Susan M. Swetter
    Susan M. Swetter

    Department of Dermatology, Stanford University
    Dermatology Service, Veterans Affairs Palo Alto

  • Brett Kuprel
    Helen M. Blau
    Professor & Director

    Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University

  • Sebastian Thrun
    Sebastian Thrun
    Adjunct Professor

    Department of Computer Science, Stanford University

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