Hyperspectral microscopic imaging for the detection of head and neck squamous cell carcinoma on histologic slides

The purpose of this study is to explore the feasibility of using hyperspectral imaging (HSI) for automatic detection of head and neck squamous cell carcinoma (SCC) in histologic images. Histologic slides from 14 patients with SCC of the larynx, hypopharynx, and buccal mucosa were scanned to train and test an Inception-based two-dimensional convolutional neural network (CNN). The average accuracy, sensitivity and specificity of the HSI patch-based CNN classification were 0.80, 0.82 and 0.78, respectively. The hyperspectral microscopic imaging and proposed classification method provide an automatic tool to aid pathologists in detecting SCC on histologic slides.

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