20.500.12556/RUL-169111 Skin lesion classification in head and neck cancers using tissue index images derived from hyperspectral imaging Background: Skin lesions associated with head and neck carcinomas present a diagnostic challenge. Conventional imaging methods, such as dermoscopy and RGB imaging, often face limitations in providing detailed information about skin lesions and accurately differentiating tumor tissue from healthy skin. Methods: This study developed a novel approach utilizing tissue index images derived from hyperspectral imaging (HSI) in combination with machine learning (ML) classifiers to enhance lesion classification. The primary aim was to identify essential features for categorizing tumor, peritumor, and healthy skin regions using both RGB and hyperspectral data. Detailed skin lesion images of 16 patients, comprising 24 lesions, were acquired using HSI. The first- and second-order statistics radiomic features were extracted from both the tissue index images and RGB images, with the minimum redundancy–maximum relevance (mRMR) algorithm used to select the most relevant ones that played an important role in improving classification accuracy and offering insights into the complexities of skin lesion morphology. We assessed the classification accuracy across three scenarios: using only RGB images (Scenario I), only tissue index images (Scenario II), and their combination (Scenario III). Results: The results indicated an accuracy of 87.73% for RGB images alone, which improved to 91.75% for tissue index images. The area under the curve (AUC) for lesion classifications reached 0.85 with RGB images and over 0.94 with tissue index images. Conclusions: These findings underscore the potential of utilizing HSI-derived tissue index images as a method for the non-invasive characterization of tissues and tumor analysis. medical physics hyperspectral imaging tissue index images tumors machine learning medicinska fizika hiperspektralno slikanje tumorji strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2025-05-13 08:27:21 2025-05-13 08:27:22 2025-05-14 04:01:34 0000-00-00 00:00:00 2025 0 0 27 str. iss. 10, art. no. 1622 Vol. 17 May 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616-073:53 2072-6694 10.3390/cancers17101622 235622147 cancers-17-01622_(1).pdf cancers-17-01622_(1).pdf 1 A8049ADCD42A79975AA7EE4F0DF50F0C 29fa7ffffa03de0801cb725c0dd72c881785dd4a9ab44535b62e7454eba0ec6e f379de16-2fc2-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=210277 https://www.mdpi.com/2072-6694/17/10/1622 0 fb854dca-2fc2-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=210278 Fakulteta za matematiko in fiziko Zdravstvena fakulteta 0 0 0