Szczegóły publikacji
Opis bibliograficzny
Melanoma thickness prediction based on convolutional neural network with VGG-19 model transfer learning / Joanna JAWOREK-KORJAKOWSKA, Paweł KŁECZEK, Marek GORGOŃ // W: CVPR 2019 [Dokument elektroniczny] : 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition : 16–20 June 2019, Long Beach, California : proceedings. — Wersja do Windows. — Dane teksowe. — Piscataway : IEEE, cop. 2019. — (Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) ; ISSN 2575-7075). — e-ISBN: 978-1-7281-3293-8. — S. 2748–2756. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 2755–2756, Abstr.
Autorzy (3)
Dane bibliometryczne
| ID BaDAP | 128309 |
|---|---|
| Data dodania do BaDAP | 2020-04-15 |
| Tekst źródłowy | URL |
| DOI | 10.1109/CVPRW.2019.00333 |
| Rok publikacji | 2019 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE Conference on Computer Vision and Pattern Recognition 2019 |
| Czasopismo/seria | Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Abstract
Over the past two decades, malignant melanoma incidence rate has dramatically risen but melanoma mortality has only recently stabilized. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Thickness is one of the most important factor in melanoma prognosis and it is used to establish the size of the surgical margin, as well as to select patients for sentinel lymph node biopsy. However, little work has concentrated on the evaluation of melanoma thickness both from the clinical as well as computer-aided diagnostic side. To address this problem, we propose an effective computer-vision based machine learning tool that can perform the preoperative evaluation of melanoma thickness. The novelty of our approach is that we directly predict the thickness of the skin lesion into one of three classes: less than 0.75 mm, 0.76-1.5 mm, and greater that 1.5 mm. In this study, we use transfer learning of the pre-trained, adapted to our application VGG-19 convolutional neural network (CNN) with an adjusted densely-connected classifier. Due to the limited data we investigate the transfer learning method where we apply knowledge from model trained on a different task. Our database contains 244 dermoscopy images. Experiments confirm the developed algorithm's ability to classify skin lesion thickness with 87.2% overall accuracy what is a state-of-the-art result in melanoma thickness prediction.
