Academic Journal
Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review
| العنوان: | Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review |
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| المؤلفون: | Höhn, Julia, Hekler, Achim, Krieghoff-Henning, Eva, Kather, Jakob Nikolas, Utikal, Jochen Sven, Meier, Friedegund, Gellrich, Frank Friedrich, Hauschild, Axel, French, Lars, Schlager, Justin Gabriel, Ghoreschi, Kamran, Wilhelm, Tabea, Kutzner, Heinz, Heppt, Markus, Haferkamp, Sebastian, Sondermann, Wiebke, Schadendorf, Dirk, Schilling, Bastian, Maron, Roman C, Schmitt, Max, Jutzi, Tanja, Fröhling, Stefan, Lipka, Daniel B, Brinker, Titus Josef |
| سنة النشر: | 2021 |
| المجموعة: | Würzburg University: Online Publication Service |
| مصطلحات موضوعية: | ddc:610 |
| الوصف: | 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| الاتاحة: | https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/35341 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-353417 https://opus.bibliothek.uni-wuerzburg.de/files/35341/jmir-2021-7-e20708.pdf |
| Rights: | https://creativecommons.org/licenses/by/4.0/deed.de ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.528E20E9 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |