Szczegóły publikacji
Opis bibliograficzny
Convolutional Neural Network approach to classify skin lesions using reflectance confocal microscopy / Marek WODZIŃSKI, Andrzej SKALSKI, Alexander Witkowski, Giovanni Pellacani, Joanna Ludzik // W: EMBC 2019 [Dokument elektroniczny] : 41st annual international conference of the IEEE Engineering in Medicine and Biology Society : 23-27 July 2019, Berlin, Germany. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2019. — (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; ISSN 1094-687X). — ISBN: 978-1-5386-1312-2; e-ISBN: 978-1-5386-1311-5. — S. 4754–4757. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 4757, Abstr.
Autorzy (5)
- AGHWodziński Marek
- AGHSkalski Andrzej
- Witkowski Alexander
- Pellacani Giovanni
- Ludzik Joanna
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 125186 |
|---|---|
| Data dodania do BaDAP | 2019-10-16 |
| Tekst źródłowy | URL |
| DOI | 10.1109/EMBC.2019.8856731 |
| Rok publikacji | 2019 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | The Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019 |
| Czasopismo/seria | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abstract
We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both morbidity and mortality. RCM is an in-vivo non-invasive screening tool that produces virtual biopsies of skin lesions but its proficient and safe use requires hard to obtain expertise. Therefore, it may be useful to have an additional tool to aid diagnosis. The proposed network is based on the ResNet architecture. The dataset consists of 429 RCM mosaics and is divided into 3 classes: melanoma, basal cell carcinoma, and benign naevi with the ground-truth confirmed by a histopathological examination. The test set classification accuracy was 87%, higher than the accuracy achieved by medical, confocal users. The results show that the proposed classification system can be a useful tool to aid in early, noninvasive melanoma detection.
