Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images
Abstract Background Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effecti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-022-00871-w |
| _version_ | 1828437780510801920 |
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| author | James Ren Hou Lee Maya Pavlova Mahmoud Famouri Alexander Wong |
| author_facet | James Ren Hou Lee Maya Pavlova Mahmoud Famouri Alexander Wong |
| author_sort | James Ren Hou Lee |
| collection | DOAJ |
| description | Abstract Background Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective early detection with key screening approaches such as dermoscopy examinations, leading to stronger recovery prognoses. Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce Cancer-Net SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public. To the best of the authors’ knowledge, Cancer-Net SCa comprises the first machine-driven design of deep neural network architectures tailored specifically for skin cancer detection, one of which leverages attention condensers for an efficient self-attention design. Results We investigate and audit the behaviour of Cancer-Net SCa in a responsible and transparent manner through explainability-driven performance validation. All the proposed designs achieved improved accuracy when compared to the ResNet-50 architecture while also achieving significantly reduced architectural and computational complexity. In addition, when evaluating the decision making process of the networks, it can be seen that diagnostically relevant critical factors are leveraged rather than irrelevant visual indicators and imaging artifacts. Conclusion The proposed Cancer-Net SCa designs achieve strong skin cancer detection performance on the International Skin Imaging Collaboration (ISIC) dataset, while providing a strong balance between computation and architectural efficiency and accuracy. While Cancer-Net SCa is not a production-ready screening solution, the hope is that the release of Cancer-Net SCa in open source, open access form will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them. |
| first_indexed | 2024-12-10T19:51:41Z |
| format | Article |
| id | doaj.art-6b03e6b5e4c440289f4fa656168fdcd5 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-12-10T19:51:41Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-6b03e6b5e4c440289f4fa656168fdcd52022-12-22T01:35:46ZengBMCBMC Medical Imaging1471-23422022-08-0122111210.1186/s12880-022-00871-wCancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy imagesJames Ren Hou Lee0Maya Pavlova1Mahmoud Famouri2Alexander Wong3Vision and Image Processing Research Group, University of WaterlooVision and Image Processing Research Group, University of WaterlooDarwinAI CorpVision and Image Processing Research Group, University of WaterlooAbstract Background Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective early detection with key screening approaches such as dermoscopy examinations, leading to stronger recovery prognoses. Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce Cancer-Net SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public. To the best of the authors’ knowledge, Cancer-Net SCa comprises the first machine-driven design of deep neural network architectures tailored specifically for skin cancer detection, one of which leverages attention condensers for an efficient self-attention design. Results We investigate and audit the behaviour of Cancer-Net SCa in a responsible and transparent manner through explainability-driven performance validation. All the proposed designs achieved improved accuracy when compared to the ResNet-50 architecture while also achieving significantly reduced architectural and computational complexity. In addition, when evaluating the decision making process of the networks, it can be seen that diagnostically relevant critical factors are leveraged rather than irrelevant visual indicators and imaging artifacts. Conclusion The proposed Cancer-Net SCa designs achieve strong skin cancer detection performance on the International Skin Imaging Collaboration (ISIC) dataset, while providing a strong balance between computation and architectural efficiency and accuracy. While Cancer-Net SCa is not a production-ready screening solution, the hope is that the release of Cancer-Net SCa in open source, open access form will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.https://doi.org/10.1186/s12880-022-00871-wAkin cancerMelanomaDeep neural networkSelf-attention |
| spellingShingle | James Ren Hou Lee Maya Pavlova Mahmoud Famouri Alexander Wong Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images BMC Medical Imaging Akin cancer Melanoma Deep neural network Self-attention |
| title | Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images |
| title_full | Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images |
| title_fullStr | Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images |
| title_full_unstemmed | Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images |
| title_short | Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images |
| title_sort | cancer net sca tailored deep neural network designs for detection of skin cancer from dermoscopy images |
| topic | Akin cancer Melanoma Deep neural network Self-attention |
| url | https://doi.org/10.1186/s12880-022-00871-w |
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