4 research outputs found
The role of AI classifiers in skin cancer images
Background: The use of different imaging modalities to assist in skin cancer diagnosis
is a common practice in clinical scenarios. Different features representative of the lesion
under evaluation can be retrieved from image analysis and processing. However,
the integration and understanding of these additional parameters can be a challenging
task for physicians, so artificial intelligence (AI) methods can be implemented to
assist in this process. This bibliographic research was performed with the goal of
assessing the current applications of AI algorithms as an assistive tool in skin cancer
diagnosis, based on information retrieved from different imaging modalities.
Materials and methods: The bibliography databases ISI Web of Science, PubMed and
Scopus were used for the literature search, with the combination of keywords: skin
cancer, skin neoplasm, imaging and classification methods.
Results: The search resulted in 526 publications, which underwent a screening process,
considering the established eligibility criteria. After screening, only 65 were
qualified for revision.
Conclusion: Different imaging modalities have already been coupled with AI methods,
particularly dermoscopy for melanoma recognition. Learners based on support
vector machines seem to be the preferred option. Future work should focus on image
analysis, processing stages and image fusion assuring the best possible classification
outcome.info:eu-repo/semantics/publishedVersio