(This article is an excerpt of the master\u2019s thesis of Stefanie Marx supervised by Department of Computer Science Faculty of Mathematics and Natural Sciences, University of Cologne, inovex GmbH and the Department of Otolaryngology, Head and Neck Surgery of Helios Dr. Horst Schmidt Kliniken Wiesbaden)<\/i><\/p>\n
Detecting cancer early and precisely is critical for improving patient outcomes. Diagnosing and treating head and neck tumors are challenging\u00a0 <\/span>for surgeons\u00a0 <\/span>pathologists. Confocal Laser Endomicroscopy (CLE) is an emerging technology in this field that offers promising advantages. This high-resolution imaging technique allows clinicians to examine tissue at the cellular level in real-time \u2013 without the need for traditional biopsy followed by histopathological examination.<\/p>\n Still, a critical problem remains: interpreting CLE images requires substantial expertise and extensive training for medical professionals. This high barrier to entry limits the widespread adoption of this valuable technology in clinical practice.<\/p>\n In this master’s thesis, I investigated how Deep Learning (DL) can be applied to automate the classification of CLE images, supporting the diagnosis of head and neck tumors. The primary objective was to develop an AI-based system capable of reliably distinguishing between healthy and neoplastic (tumorous) tissue.<\/p>\n <\/p>\n<\/div>