Classification of Skin Lesion Images With Deep Learning Approaches
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Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Latvia
Open Access Color
GOLD
Green Open Access
Yes
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Publicly Funded
No
Abstract
Skin cancer is one of the most dangerous cancer types in the world. Like any other cancer type, early detection is the key factor for the patient's recovery. Integration of artificial intelligence with medical image processing can aid to decrease misdiagnosis. The purpose of the article is to show that deep learning-based image classification can aid doctors in the healthcare field for better diagnosis of skin lesions. VGG16 and ResNet50 architectures were chosen to examine the effect of CNN networks on the classification of skin cancer types. For the implementation of these networks, the ISIC 2019 Challenge has been chosen due to the richness of data. As a result of the experiments, confusion matrices were obtained and it was observed that ResNet50 architecture achieved 91.23% accuracy and VGG16 architecture 83.89% accuracy. The study shows that deep learning methods can be sufficiently exploited for skin lesion image classification. © 2022 Baltic Journal of Modern Computing. All rights reserved.
Description
ORCID
Keywords
Deep learning, Isic 2019, Resnet50, Image classification, Vgg16, Deep Learning, Image classification, ISIC 2019, VGG16, ResNet50
Turkish CoHE Thesis Center URL
Fields of Science
Citation
Bayram, B., Kulavuz, B., Ertugrul, B., Bayram, B., Bakirman, T., Cakar, T., & Doğan, M. (2022). Classification of Skin Lesion Images with Deep Learning Approaches. Baltic Journal of Modern Computing, 10(2), pp. 241-250. https://doi.org/10.22364/bjmc.2022.10.2.10
WoS Q
Q4
Scopus Q
Q3

OpenCitations Citation Count
N/A
Source
Baltic Journal of Modern Computing
Volume
10
Issue
2
Start Page
241
End Page
250
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Citations
Scopus : 4
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Mendeley Readers : 33
SCOPUS™ Citations
4
checked on Feb 03, 2026
Page Views
260
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Downloads
13989
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Google Scholar™

OpenAlex FWCI
0.44689409
Sustainable Development Goals
3
GOOD HEALTH AND WELL-BEING

4
QUALITY EDUCATION

16
PEACE, JUSTICE AND STRONG INSTITUTIONS

17
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