Research on a Burn Severity Detection Method Based on Hyperspectral Imaging
The accurate detection of burn wounds is a key research direction in the field of burn medicine, as diagnostic results directly influence the risk of wound infection and the formation of hypertrophic scars. Currently, burn diagnosis is primarily dependent on the clinical judgment of physicians, but...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1330 |
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| author | Sijia Wang Minghui Gu Mingle Zhang Xin Tan |
| author_facet | Sijia Wang Minghui Gu Mingle Zhang Xin Tan |
| author_sort | Sijia Wang |
| collection | DOAJ |
| description | The accurate detection of burn wounds is a key research direction in the field of burn medicine, as diagnostic results directly influence the risk of wound infection and the formation of hypertrophic scars. Currently, burn diagnosis is primarily dependent on the clinical judgment of physicians, but its accuracy is typically only between 65% and 70%. Therefore, a non-invasive, efficient method for burn severity assessment is urgently needed. Hyperspectral imaging (HSI), as a non-invasive and contactless spectral detection technique, has been shown to precisely monitor structural changes in burn-affected skin tissue and holds significant potential for burn depth diagnosis. However, research on the application of burn severity detection remains relatively limited, which restricts its widespread use in clinical settings. A burn severity detection classification network (MBNet) based on the Mamba model is proposed in this paper. Through a bidirectional scanning strategy, MBNet effectively captures the long-term dependencies of spectral features, accurately establishes the relationships between bands, and efficiently distinguishes subtle spectral differences under different burn conditions. MBNet provides a reliable and efficient method for clinical burn severity assessment. A comparison of MBNet with seven typical machine learning algorithms on a custom dataset demonstrates that MBNet significantly outperforms these methods in terms of accuracy. |
| format | Article |
| id | doaj-art-de7fa249ae96415eae36fcbdcc284330 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-de7fa249ae96415eae36fcbdcc2843302025-08-20T02:06:15ZengMDPI AGSensors1424-82202025-02-01255133010.3390/s25051330Research on a Burn Severity Detection Method Based on Hyperspectral ImagingSijia Wang0Minghui Gu1Mingle Zhang2Xin Tan3Department of Burn and Plastic Surgery, Jilin Provincial People’s Hospital, Changchun 130021, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaThe accurate detection of burn wounds is a key research direction in the field of burn medicine, as diagnostic results directly influence the risk of wound infection and the formation of hypertrophic scars. Currently, burn diagnosis is primarily dependent on the clinical judgment of physicians, but its accuracy is typically only between 65% and 70%. Therefore, a non-invasive, efficient method for burn severity assessment is urgently needed. Hyperspectral imaging (HSI), as a non-invasive and contactless spectral detection technique, has been shown to precisely monitor structural changes in burn-affected skin tissue and holds significant potential for burn depth diagnosis. However, research on the application of burn severity detection remains relatively limited, which restricts its widespread use in clinical settings. A burn severity detection classification network (MBNet) based on the Mamba model is proposed in this paper. Through a bidirectional scanning strategy, MBNet effectively captures the long-term dependencies of spectral features, accurately establishes the relationships between bands, and efficiently distinguishes subtle spectral differences under different burn conditions. MBNet provides a reliable and efficient method for clinical burn severity assessment. A comparison of MBNet with seven typical machine learning algorithms on a custom dataset demonstrates that MBNet significantly outperforms these methods in terms of accuracy.https://www.mdpi.com/1424-8220/25/5/1330burn detectionhyperspectral imagingMambabidirectional scanningmachine learning |
| spellingShingle | Sijia Wang Minghui Gu Mingle Zhang Xin Tan Research on a Burn Severity Detection Method Based on Hyperspectral Imaging Sensors burn detection hyperspectral imaging Mamba bidirectional scanning machine learning |
| title | Research on a Burn Severity Detection Method Based on Hyperspectral Imaging |
| title_full | Research on a Burn Severity Detection Method Based on Hyperspectral Imaging |
| title_fullStr | Research on a Burn Severity Detection Method Based on Hyperspectral Imaging |
| title_full_unstemmed | Research on a Burn Severity Detection Method Based on Hyperspectral Imaging |
| title_short | Research on a Burn Severity Detection Method Based on Hyperspectral Imaging |
| title_sort | research on a burn severity detection method based on hyperspectral imaging |
| topic | burn detection hyperspectral imaging Mamba bidirectional scanning machine learning |
| url | https://www.mdpi.com/1424-8220/25/5/1330 |
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