A1 Journal article (refereed)
Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas : A Pilot Study (2021)

Räsänen, J., Salmivuori, M., Pölönen, I., Grönroos, M., & Neittaanmäki, N. (2021). Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas : A Pilot Study. Acta Dermato-Venereologica, 101(2), Article adv00405. https://doi.org/10.2340/00015555-3755

JYU authors or editors

Publication details

All authors or editors: Räsänen, Janne; Salmivuori, Mari; Pölönen, Ilkka; Grönroos, Mari; Neittaanmäki, Noora

Journal or series: Acta Dermato-Venereologica

ISSN: 0001-5555

eISSN: 1651-2057

Publication year: 2021

Volume: 101

Issue number: 2

Article number: adv00405

Publisher: Society for Publication of Acta Dermato-Venereologica

Publication country: Sweden

Publication language: English

DOI: https://doi.org/10.2340/00015555-3755

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/75240

Abstract

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.

Keywords: skin cancer; basal cell carcinoma; diagnostics; spectral imaging; machine learning; neural networks (information technology)

Free keywords: deep learning; neural network; basal cell carcinoma; malignant melanoma

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2021

JUFO rating: 2