title: Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data
metadata.name: dc.contributor.author
metadata.value: Lyakhova, Ulyana A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Lyakhov, Pavel A.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-08-19T21:01:08Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-08-19T21:01:08Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2024-07
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Lyakhova U. А., Lyakhov P. А. Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2024, vol. 20, iss. 2, pp. 231–243. https://doi.org/10.21638/spbu10.2024.208 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu10.2024.208
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/47269
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: Today, skin cancer is one of the leading causes of death in the world. Diagnosing skin cancer
  early is critical to increasing potential survival. Therefore, it is relevant to develop highprecision
  intelligent auxiliary diagnostic systems for detecting skin cancer in the early stages.
  Ensemble learning is one of the current and promising methods for increasing the accuracy
  of intelligent classification systems by reducing the dispersion and variability of predictions
  of individual components of the overall system. The work proposes an ensemble intelligent
  system for analyzing heterogeneous dermatological data based on multimodal neural networks.
  The accuracy of the developed ensemble system was 85.92%, which is 1.85 percentage
  points higher than the average accuracy of individual multimodal architectures for classifying
  heterogeneous dermatological data. The developed system can be used as a high-precision
  auxiliary diagnostic tool to help make a medical decision, which will increase the chance of
  early detection of pigmented oncological pathologies.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: The research in section 2 was supported in the North-Caucasus Center for Mathematical Research
  under an agreement with the Ministry of Science and Higher Education of the Russian Federation
  (agreement N 075-02-2024-1451), the research in section 3 was supported by the Russian Science
  Foundation, project N 23-71-10013 (https://rscf.ru/project/23-71-10013/). The authors thank the North-
  Caucasus Federal University for assistance within the framework of the project to support small scientific
  groups and individual scientists.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 20; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: multimodal neural network
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: ensemble neural network
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: machine learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: heterogeneous data
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: dermatological images
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: pigmented skin lesions
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: skin cancer
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: melanoma
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Multimodal ensemble neural network system for skin cancer detection on heterogeneous dermatological data
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: vestnik_spbu10_2024_231.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/47269/1/vestnik_spbu10_2024_231.pdf
resources.format: Adobe PDF
resources.size: 1,64 MB