التفاصيل البيبلوغرافية
| العنوان: |
Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review |
| المؤلفون: |
Nazari, Sana, García Campos, Rafael |
| المساهمون: |
European Commission |
| المصدر: |
Life, 2023, vol. 13, núm. 11, p. 2123 ; Articles publicats (D-ATC) |
| بيانات النشر: |
MDPI (Multidisciplinary Digital Publishing Institute) |
| سنة النشر: |
2023 |
| المجموعة: |
Universitat de Girona: DUGiDocs (UdG Digital Repository) |
| مصطلحات موضوعية: |
Pell -- Càncer, Imatgeria mèdica, Imaging systems in medicine, Visió per ordinador en medicina, Computer vision in medicine, Pell -- Càncer -- Imatgeria, Skin -- Cancer -- Imaging, Skin -- Cancer, Càncer -- Detecció precoç, Cancer -- Early detection |
| الوصف: |
Skin cancer has become increasingly common over the past decade, with melanoma being the most aggressive type. Hence, early detection of skin cancer and melanoma is essential in dermatology. Computational methods can be a valuable tool for assisting dermatologists in identifying skin cancer. Most research in machine learning for skin cancer detection has focused on dermoscopy images due to the existence of larger image datasets. However, general practitioners typically do not have access to a dermoscope and must rely on naked-eye examinations or standard clinical images. By using standard, off-the-shelf cameras to detect high-risk moles, machine learning has also proven to be an effective tool. The objective of this paper is to provide a comprehensive review of image-processing techniques for skin cancer detection using clinical images. In this study, we evaluate 51 state-of-the-art articles that have used machine learning methods to detect skin cancer over the past decade, focusing on clinical datasets. Even though several studies have been conducted in this field, there are still few publicly available clinical datasets with sufficient data that can be used as a benchmark, especially when compared to the existing dermoscopy databases. In addition, we observed that the available artifact removal approaches are not quite adequate in some cases and may also have a negative impact on the models. Moreover, the majority of the reviewed articles are working with single-lesion images and do not consider typical mole patterns and temporal changes in the lesions of each patient ; This work was partially funded through the European Commission’s Horizon 2020 program as part of the iToBoS project (grant number SC1-BHC-06-2020-965221) |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/eissn/2075-1729; info:eu-repo/grantAgreement/EC/H2020/965221/EU/Intelligent Total Body Scanner for Early Detection of Melanoma/iToBoS; http://hdl.handle.net/10256/23729 |
| الاتاحة: |
http://hdl.handle.net/10256/23729 |
| Rights: |
Attribution 4.0 International ; http://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.4F5ED28B |
| قاعدة البيانات: |
BASE |