research-article

Background selection schema on deep learning-based classification of dermatological disease

Authors:

Shuang ZhaoAuthors Info & Claims

Volume 149, Issue C

https://doi.org/10.1016/j.compbiomed.2022.105966

Published: 01 October 2022 Publication History

Abstract

Skin diseases are one of the most common ailments affecting humans. Artificial intelligence based on deep learning can significantly improve the efficiency of identifying skin disorders and alleviate the scarcity of medical resources. However, the distribution of background information in dermatological datasets is imbalanced, causing generalized deep learning models to perform poorly in skin disease classification. We propose a deep learning schema that combines data preprocessing, data augmentation, and residual networks to study the influence of color-based background selection on a deep model’s capacity to learn foreground lesion subject attributes in a skin disease classification problem. First, clinical photographs are annotated by dermatologists, and then the original background information is masked with unique colors to generate several subsets with distinct background colors. Sample-balanced training and test sets are generated using random over/undersampling and data augmentation techniques. Finally, the deep learning networks are independently trained on diverse subsets of backdrop colors to compare the performance of classifiers based on different background information. Extensive experiments demonstrate that color-based background information significantly affects the classification of skin diseases and that classifiers trained on the green subset achieve state-of-the-art performance for classifying black and red skin lesions.

Highlights

•

This study has given us new insights into skin diseases in Asian populations.

•

This study presents a new method of exploring the skin disease classification.

•

This study will help improve the accuracy of skin disease classification.

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Cited By

Yousefi SNajjar-Ghabel SDanehchin RBand SHsu CMosavi A(2024)Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic imagesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10194436:2Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.jksuci.2024.101944
Nie QLi CYang JYao YSun HJiang TGrzegorzek MChen AChen HHu WLi RZhang JWang D(2024)OII-DSComputers in Biology and Medicine10.1016/j.compbiomed.2023.107620167:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107620
Huang WZhang HGuo HLi WQuan XZhang Y(2023)ADDNSComputers in Biology and Medicine10.1016/j.compbiomed.2023.107531166:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107531
Show More Cited By

Index Terms

Background selection schema on deep learning-based classification of dermatological disease
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 149, Issue C

Oct 2022

1186 pages

ISSN:0010-4825

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 October 2022

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Cited By

Yousefi SNajjar-Ghabel SDanehchin RBand SHsu CMosavi A(2024)Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic imagesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10194436:2Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.jksuci.2024.101944
Nie QLi CYang JYao YSun HJiang TGrzegorzek MChen AChen HHu WLi RZhang JWang D(2024)OII-DSComputers in Biology and Medicine10.1016/j.compbiomed.2023.107620167:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107620
Huang WZhang HGuo HLi WQuan XZhang Y(2023)ADDNSComputers in Biology and Medicine10.1016/j.compbiomed.2023.107531166:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107531
Li WLu WChu JTian QFan F(2023)Confidence-guided mask learning for semi-supervised medical image segmentationComputers in Biology and Medicine10.1016/j.compbiomed.2023.107398165:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107398
Zhao FLi DLuo RLiu MJiang XHu J(2023)Self-supervised deep learning for joint 3D low-dose PET/CT image denoisingComputers in Biology and Medicine10.1016/j.compbiomed.2023.107391165:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107391

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