A Framework for Automatic Burn Image Segmentation and Burn Depth Diagnosis Using Deep Learning
| Autor: | Cheng Siyi, Liu Hao, Yue Keqiang, Zhihui Fu, Li Wenjun |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Article Subject
Databases Factual Computer science Computer applications to medicine. Medical informatics R858-859.7 02 engineering and technology General Biochemistry Genetics and Molecular Biology Network output 03 medical and health sciences High morbidity 0302 clinical medicine Deep Learning Image Interpretation Computer-Assisted 0202 electrical engineering electronic engineering information engineering Photography Humans Segmentation General Immunology and Microbiology Burn depth business.industry Applied Mathematics Deep learning Computational Biology 030208 emergency & critical care medicine Pattern recognition General Medicine Image segmentation Modeling and Simulation 020201 artificial intelligence & image processing Artificial intelligence business Burns Research Article |
| Zdroj: | Computational and Mathematical Methods in Medicine Computational and Mathematical Methods in Medicine, Vol 2021 (2021) |
| ISSN: | 1748-6718 1748-670X |
| Popis: | Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds and burn depth, which can save lives in some cases. However, due to the complexity of burn wounds, the early diagnosis of burns lacks accuracy and difference. Therefore, we use deep learning technology to automate and standardize burn diagnosis to reduce human errors and improve burn diagnosis. First, the burn dataset with detailed burn area segmentation and burn depth labelling is created. Then, an end-to-end framework based on deep learning method for advanced burn area segmentation and burn depth diagnosis is proposed. The framework is firstly used to segment the burn area in the burn images. On this basis, the calculation of the percentage of the burn area in the total body surface area (TBSA) can be realized by extending the network output structure and the labels of the burn dataset. Then, the framework is used to segment multiple burn depth areas. Finally, the network achieves the best result with IOU of 0.8467 for the segmentation of burn and no burn area. And for multiple burn depth areas segmentation, the best average IOU is 0.5144. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text