Please use this identifier to cite or link to this item: https://dspace.ncfu.ru/handle/20.500.12258/25248
Title: Cloud-Based Service for Recognizing Pigmented Skin Lesions Using a Multimodal Neural Network System
Authors: Lyakhova, U. A.
Ляхова, У. А.
Bondarenko, D. N.
Бондаренко, Д. Н.
Boyarskaya, E. E.
Боярская, Э. Е.
Nagornov, N. N.
Нагорнов, Н. Н.
Keywords: Heterogeneous data;Skin cancer;Cloud-based system;Melanoma;Multimodal neural networks;Pigmented skin lesions
Issue Date: 2023
Citation: Lyakhova, U.A., Bondarenko, D.N., Boyarskaya, E.E., Nagornov, N.N. Cloud-Based Service for Recognizing Pigmented Skin Lesions Using a Multimodal Neural Network System // Lecture Notes in Networks and Systems. - 2023. - 702 LNNS, pp. 401-409. - DOI: 10.1007/978-3-031-34127-4_39
Series/Report no.: Lecture Notes in Networks and Systems
Abstract: Skin cancer is the most common cancer in humans today and is usually caused by exposure to ultraviolet radiation. There are many diagnostic methods for visual analysis of pigmented neoplasms. However, most of these methods are subjective and largely dependent on the experience of the clinician. To minimize the influence of the human factor, it is proposed to introduce artificial intelligence technologies that have made it possible to reach new heights in terms of the accuracy of classifying medical data, including in the field of dermatology. Artificial intelligence technologies can equal and even surpass the capabilities of an dermatologists in terms of the accuracy of visual diagnostics. The article proposes a web application based on a multimodal neural network system for recognizing pigmented skin lesions as an additional auxiliary tool for oncologist. The system combines and analyzes heterogeneous dermatological data, which are images of pigmented neoplasms and such statistical information about the patient as age, gender, and localization of pigmented skin lesions. The recognition accuracy of the proposed web application was 85.65%. The use of the proposed web application as an auxiliary diagnostic method will expand the possibilities of early detection of skin cancer and minimize the impact of the human factor.
URI: http://hdl.handle.net/20.500.12258/25248
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

Files in This Item:
File SizeFormat 
scopusresults 2715 .pdf
  Restricted Access
131.93 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.