Background selection schema on deep learning-based classification of dermatological disease
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.
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End-to-end convolutional neural networks have dominated performance at diverse medical imaging tasks.
“Motivated by the dominant performance of CNNs, it is desirable to develop an end-to-end network for automatic placental maturity grading (PMG) from ultrasound images.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [12]
Deep learning algorithms have shown remarkable success in medical image analysis due to their ability to learn complex features from limited data.
“Deep learning algorithms, on the other hand, have shown remarkable success in these tasks due to their ability to learn complex features from limited data [12 -14].”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [12]
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DM-UNet: a Dilated Multi-Scale and Bottleneck Attention UNet for Ultrasound Image Segmentation
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Mamba-HTEPA: A multi-branch structure framework for multimodal grading of meningiomas using Mamba
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- Authors
- Zhou, J., Wu, Z., Jiang, Z., Huang, K., Guo, K., & Zhao, S.
- Affiliations
- Central South University (CN)
- Corresponding author
- Guo, Kehua
- Zhao, Shuang
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 149
- Pages
- 105966
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W4292136262
- Cited by
- 53
- References
- 54
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 15 · 2023: 20 · 2022: 1
- FWCI
- 5.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Dermatology › Medicine and Dermatology Studies History
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- skin disease classification, background selection, class imbalance, data augmentation, oversampling and undersampling, ResNet, dermatological images, skin lesion detection
- MeSH terms
- Machine Learning, Deep Learning, Artificial Intelligence, Humans, Skin Diseases, Neural Networks, Computer
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