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Convolutional neural networks in skin cancer detection using spatial and spectral domain
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Paper Abstract

Skin cancers are a world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive screening. In this study we compare how use of both spectral and spatial domain increase classification performance of convolutional neural networks. We compare five different neural network architectures for real patient data. Our models gain same or slightly better positive predictive value as clinicians. Towards more general and reliable model more data is needed and collection of training data should be systematic.

Paper Details

Date Published: 26 February 2019
PDF: 8 pages
Proc. SPIE 10851, Photonics in Dermatology and Plastic Surgery 2019, 108510B (26 February 2019); doi: 10.1117/12.2509871
Show Author Affiliations
Ilkka Pölönen, Univ. of Jyväskylä (Finland)
Samuli Rahkonen, Univ. of Jyväskylä (Finland)
Leevi Annala, Univ. of Jyväskylä (Finland)
Noora Neittaanmäki, Göteborgs Univ. (Sweden)

Published in SPIE Proceedings Vol. 10851:
Photonics in Dermatology and Plastic Surgery 2019
Bernard Choi; Haishan Zeng, Editor(s)

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