We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning.
- Authors
Santos, Alan R. F.; Aires, Kelson R. T.; Veras, Rodrigo M. S.
- Abstract
Malignant skin cancers are common in emerging countries, with excessive sun exposure and genetic predispositions being the main causes. Variations in lighting and color, resulting from the diversity of devices and lighting conditions during image capture, pose a challenge for automated diagnosis through digital images. Deep learning techniques emerge as promising solutions to improve the accuracy of identifying malignant skin lesions. This work aims to investigate the impact of lighting and color correction methods on automated skin cancer diagnosis using deep learning architectures, focusing on the relevance of these characteristics for accuracy in identifying malignant skin cancer. The developed methodology includes steps for hair removal, lighting, and color correction, defining the region of interest, and classification using deep neural network architectures. We employed deep learning techniques such as LCDPNet, LLNeRF, and DSN for lighting and color correction, which still need to be tested in this context. The results emphasize the importance of image preprocessing, especially in lighting and color adjustments, where the best results show an accuracy increase of between 3% and 4%. We observed that different deep neural network architectures react variably to lighting and color corrections. Some architectures are more sensitive to variations in these characteristics, while others are more robust. Advanced lighting and color correction can thus significantly improve the accuracy of malignant skin cancer diagnosis.
- Subjects
ARTIFICIAL neural networks; DEEP learning; SKIN cancer; SUNSHINE; HAIR removal; DIGITAL images
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 8, p3297
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14083297