Market-approved convolutional neural network tasked with classifying skin lesions under suspicion of melanoma: performance across primary care clinics within Australia
Improvement of the performance of this CNN for the classification of images, particularly when suspecting MM is necessary before it may be used in a clinical setting in Australia. Other validated AI systems used internationally may also require review for use in an Australian setting.
Authors
- Mike Climstein (ORCID: https://orcid.org/0000-0002-5833-7760)
- Joe Walsh (ORCID: https://orcid.org/0000-0002-2983-934X)
- Paul Coxon (ORCID: https://orcid.org/0009-0005-6555-2230)
- James Furness (ORCID: https://orcid.org/0000-0001-7773-0253)
- Ian Miller (ORCID: https://orcid.org/0000-0002-1372-7626)
- Michael Stapelberg
- Nedeljka Rosić (ORCID: https://orcid.org/0000-0002-2598-7046)
- Jeremy Hudson
- Nathaniel Milani
Institutions
- Bond University (AU)
- The University of Sydney (AU)
- Robina Hospital (AU)
- Townsville Hospital (AU)
- Southern Cross University (AU)
Publication Details
- Journal
- PeerJ
- Published
- 2025-08-28
- DOI
- https://doi.org/10.7717/peerj.19876
- Primary Topic
- Cutaneous Melanoma Detection and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Australian Government
- Johnson and Johnson