Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal Microscopy
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914769844305920 |
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| author | Yoon, Hong-Jun Keum, Chris Witkowski, Alexander Ludzik, Joanna Petrie, Tracy Hanson, Heidi A. Leachman, Sancy A. |
| author_facet | Yoon, Hong-Jun Keum, Chris Witkowski, Alexander Ludzik, Joanna Petrie, Tracy Hanson, Heidi A. Leachman, Sancy A. |
| contents | Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique used in biomedical research and clinical dermatology. It provides virtual high-resolution images of the skin and superficial tissues, reducing the need for physical biopsies. RCM employs a laser light source to illuminate the tissue, capturing the reflected light to generate detailed images of microscopic structures at various depths. Recent studies explored AI and machine learning, particularly CNNs, for analyzing RCM images. Our study proposes a segmentation strategy based on textural features to identify clinically significant regions, empowering dermatologists in effective image interpretation and boosting diagnostic confidence. This approach promises to advance dermatological diagnosis and treatment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16080 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal Microscopy Yoon, Hong-Jun Keum, Chris Witkowski, Alexander Ludzik, Joanna Petrie, Tracy Hanson, Heidi A. Leachman, Sancy A. Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique used in biomedical research and clinical dermatology. It provides virtual high-resolution images of the skin and superficial tissues, reducing the need for physical biopsies. RCM employs a laser light source to illuminate the tissue, capturing the reflected light to generate detailed images of microscopic structures at various depths. Recent studies explored AI and machine learning, particularly CNNs, for analyzing RCM images. Our study proposes a segmentation strategy based on textural features to identify clinically significant regions, empowering dermatologists in effective image interpretation and boosting diagnostic confidence. This approach promises to advance dermatological diagnosis and treatment. |
| title | Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal Microscopy |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.16080 |