Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review (Preprint) Article Swipe
Julia Höhn
,
Achim Hekler
,
Eva Krieghoff‐Henning
,
Jakob Nikolas Kather
,
Jochen Utikal
,
Friedegund Meier
,
Frank Friedrich Gellrich
,
Axel Hauschild
,
Lars E. French
,
Justin Gabriel Schlager
,
Kamran Ghoreschi
,
Tabea Wilhelm
,
Heinz Kutzner
,
Markus V. Heppt
,
Sebastian Haferkamp
,
Wiebke Sondermann
,
Dirk Schadendorf
,
Bastian Schilling
,
Roman C. Maron
,
Max Schmitt
,
Tanja Jutzi
,
Stefan Fröhling
,
Daniel B. Lipka
,
Titus J. Brinker
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.20708
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.20708
BACKGROUND Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers. OBJECTIVE This systematic review focuses on the current research investigating the impact of merging information from image features and patient data on the performance of CNN-based skin cancer image classification. This study aims to explore the potential in this field of research by evaluating the types of patient data used, the ways in which the nonimage data are encoded and merged with the image features, and the impact of the integration on the classifier performance. METHODS Google Scholar, PubMed, MEDLINE, and ScienceDirect were screened for peer-reviewed studies published in English that dealt with the integration of patient data within a CNN-based skin cancer classification. The search terms skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined. RESULTS A total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex, and lesion location. The patient data were mostly one-hot encoded. There were differences in the complexity that the encoded patient data were processed with regarding deep learning methods before and after fusing them with the image features for a combined classifier. CONCLUSIONS This study indicates the potential benefits of integrating patient data into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhance classification performance, especially in the case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for patients’ benefits.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.2196/preprints.20708
- OA Status
- gold
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4245907753
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4245907753Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/preprints.20708Digital Object Identifier
- Title
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Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review (Preprint)Work title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-27Full publication date if available
- Authors
-
Julia Höhn, Achim Hekler, Eva Krieghoff‐Henning, Jakob Nikolas Kather, Jochen Utikal, Friedegund Meier, Frank Friedrich Gellrich, Axel Hauschild, Lars E. French, Justin Gabriel Schlager, Kamran Ghoreschi, Tabea Wilhelm, Heinz Kutzner, Markus V. Heppt, Sebastian Haferkamp, Wiebke Sondermann, Dirk Schadendorf, Bastian Schilling, Roman C. Maron, Max Schmitt, Tanja Jutzi, Stefan Fröhling, Daniel B. Lipka, Titus J. BrinkerList of authors in order
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https://doi.org/10.2196/preprints.20708Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2196/preprints.20708Direct OA link when available
- Concepts
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Preprint, Convolutional neural network, Computer science, Skin cancer, Artificial intelligence, Medicine, Cancer, World Wide Web, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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19Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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