Introduction:
Improving the gold standard in the diagnosis of head and neck cancer using white light
and invasive biopsy with digital image recognition procedures, there is a need for
a development of new technologies. In the sense of an "optical biopsy", they in vivo
and online should provide additional objective information for decision making for
the head and neck surgeon. Artificial neural networks in combination with machine
learning might be a helpful and fast approach.
Material and Methods:
NIR ICG-FE was standardized in patients with head and neck cancer. Video documented
standardized tissue biopsies from the carcinomas and their adjacent mucosa were performed.
Only histologically proven images were then manually annotated pixel-accurate for
the automatic analysis. Training and testing of the automated image recognition algorithm
by deep learning methods has been done in the Leave-One-Patient-Out-procedures. As
an architecture for the neural network the widespread "AlexNet-Konfiguration" has
been used.
Results:
The "AlexNet-Konfiguration" as already trained neural network was suitable to initialize
our deep learning method. The newly designed neural network has been successfully
refined step by step for carcinoma detection. Previous results allow the conclusion
that an automatic detection of head and neck cancer is possible with the method used.
Conclusions:
Image data by NIR ICG-FE can be used in an artificial neural network by deep learning
methods to create an automated image recognition algorithm for the detection of head
and neck cancer.
