Janne Räsänen, Mari Salmivuori, Ilkka Pölönen, Mari Grönroos, Noora Neittaanmäki
DOI: 10.2340/00015555-3755

Pigmented basal cell carcinomas can be difficult to distinguish from melanocytic tumours. Hyperspectral imaging is a non-invasive imaging technique that measures the reflectance spectra of skin in vivo. The aim of this prospective pilot study was to use a convolutional neural network classifier in hyperspectral images for differential diagnosis between pigment­ed basal cell carcinomas and melanoma. A total of 26 pigmented lesions (10 pigmented basal cell carcinomas, 12 melanomas in situ, 4 invasive melanomas) were imaged with hyperspectral imaging and excised for histopatho­logical diagnosis. For 2-class classifier (melano­cytic tumours vs pigmented basal cell carcinomas) using the majority of the pixels to predict the class of the whole lesion, the results showed a sensitivity of 100% (95% confidence interval 81–100%), specificity of 90% (95% confidence interval 60–98%) and positive predictive value of 94% (95% confidence interval 73–99%). These results indicate that a convolutional neural network classifier can differentiate melanocytic tumours from pigmented basal cell carcinomas in hyperspectral images. Further studies are warranted in order to confirm these preliminary results, using larger samples and multiple tumour types, including all types of melanocytic lesions.

This is the first study to utilize hyperspectral images and a deep-learning convolutional neural network to acquire an automated diagnosis for melanocytic tumours and pigment­ed basal cell carcinomas. The results of this pilot study will serve as a basis for future research. The results indicate that, with a larger sample and training dataset, the convolutional neural network could accurately classify malignant melanocytic tumours from pigmented basal cell carcinomas. This finding may be used as the basis for development of future techniques in melanoma diagnostics, which also requires a hyperspectral camera to be commercially available to clinicians.

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