MULTICLASS SKIN LESS IONS CLASSIFICATION BASED ON DEEP NEURAL NETWORKS

Magdalena Michalska

doi:10.35784/iapgos.2963

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Published: Jun 30, 2022

DOI: https://doi.org/10.35784/iapgos.2963

DOI

https://doi.org/10.35784/iapgos.2963

Authors

Magdalena Michalska

mmagamichalska@gmail.com

Lublin University of Technology, Department of Electronics and Information Technology

http://orcid.org/0000-0002-0874-3285

Abstract

Skin diseases diagnosed with dermatoscopy are becoming more and more common. The use of computerized diagnostic systems becomes extremely effective. Non-invasive methods of diagnostics, such as deep neural networks, are an increasingly common tool studied by scientists. The article presents an overview of selected main issues related to the multi-class classification process: the stage of database selection, initial image processing, selection of the learning data set, classification tools, network training stage and obtaining final results. The described actions were implemented using available deep neural networks. The article pay attention to the final results of available models, such as effectiveness, specificity, classification accuracy for different numbers of classes and available data sets.

Keywords:

dermatoscopic images, multiclass classification, skin lesions, deep neural networks

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Michalska, M. (2022). MULTICLASS SKIN LESS IONS CLASSIFICATION BASED ON DEEP NEURAL NETWORKS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(2), 10–14. https://doi.org/10.35784/iapgos.2963

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