Skin cancer identification utilizing deep learning: A survey
Abstract Melanoma, a highly prevalent and lethal form of skin cancer, has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the early identification o...
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | IET Image Processing |
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| Online Access: | https://doi.org/10.1049/ipr2.13219 |
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| author | Dulani Meedeniya Senuri De Silva Lahiru Gamage Uditha Isuranga |
| author_facet | Dulani Meedeniya Senuri De Silva Lahiru Gamage Uditha Isuranga |
| author_sort | Dulani Meedeniya |
| collection | DOAJ |
| description | Abstract Melanoma, a highly prevalent and lethal form of skin cancer, has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the early identification of melanoma. Despite their high performance, relying solely on an image classifier undermines the credibility of the application and makes it difficult to understand the rationale behind the model's predictions highlighting the need for Explainable AI (XAI). This study provides a survey on skin cancer identification using DL techniques utilized in studies from 2017 to 2024. Compared to existing survey studies, the authors address the latest related studies covering several public skin cancer image datasets and focusing on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. The analysis and the comparisons of the existing studies will be beneficial for the researchers and developers in this area, to identify the suitable techniques to be used for automated skin cancer image classification. Thereby, the survey findings can be used to implement support applications advancing the skin cancer diagnosis process. |
| format | Article |
| id | doaj-art-97b3a4f8356f4aa1bd4e18a7cbaac7e0 |
| institution | Kabale University |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-97b3a4f8356f4aa1bd4e18a7cbaac7e02024-11-13T04:42:31ZengWileyIET Image Processing1751-96591751-96672024-11-0118133731374910.1049/ipr2.13219Skin cancer identification utilizing deep learning: A surveyDulani Meedeniya0Senuri De Silva1Lahiru Gamage2Uditha Isuranga3Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri LankaDepartment of Anatomy Yong Loo Lin School of Medicine National University of Singapore Singapore SingaporeDepartment of Computer Science and Engineering University of Moratuwa Moratuwa Sri LankaDepartment of Computer Science and Engineering University of Moratuwa Moratuwa Sri LankaAbstract Melanoma, a highly prevalent and lethal form of skin cancer, has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the early identification of melanoma. Despite their high performance, relying solely on an image classifier undermines the credibility of the application and makes it difficult to understand the rationale behind the model's predictions highlighting the need for Explainable AI (XAI). This study provides a survey on skin cancer identification using DL techniques utilized in studies from 2017 to 2024. Compared to existing survey studies, the authors address the latest related studies covering several public skin cancer image datasets and focusing on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. The analysis and the comparisons of the existing studies will be beneficial for the researchers and developers in this area, to identify the suitable techniques to be used for automated skin cancer image classification. Thereby, the survey findings can be used to implement support applications advancing the skin cancer diagnosis process.https://doi.org/10.1049/ipr2.13219computer visionimage classificationimage segmentationreviewsskin |
| spellingShingle | Dulani Meedeniya Senuri De Silva Lahiru Gamage Uditha Isuranga Skin cancer identification utilizing deep learning: A survey IET Image Processing computer vision image classification image segmentation reviews skin |
| title | Skin cancer identification utilizing deep learning: A survey |
| title_full | Skin cancer identification utilizing deep learning: A survey |
| title_fullStr | Skin cancer identification utilizing deep learning: A survey |
| title_full_unstemmed | Skin cancer identification utilizing deep learning: A survey |
| title_short | Skin cancer identification utilizing deep learning: A survey |
| title_sort | skin cancer identification utilizing deep learning a survey |
| topic | computer vision image classification image segmentation reviews skin |
| url | https://doi.org/10.1049/ipr2.13219 |
| work_keys_str_mv | AT dulanimeedeniya skincanceridentificationutilizingdeeplearningasurvey AT senuridesilva skincanceridentificationutilizingdeeplearningasurvey AT lahirugamage skincanceridentificationutilizingdeeplearningasurvey AT udithaisuranga skincanceridentificationutilizingdeeplearningasurvey |