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Article

Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

1
Information Technology Services, University of Naples “L’Orientale”, 80121 Naples, Italy
2
Department of Applied Science, I.S. Mattei Aversa M.I.U.R., 81031 Rome, Italy
*
Author to whom correspondence should be addressed.
J. Imaging 2020, 6(12), 129; https://doi.org/10.3390/jimaging6120129
Received: 9 October 2020 / Revised: 18 November 2020 / Accepted: 23 November 2020 / Published: 26 November 2020
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors. View Full-Text
Keywords: melanoma detection; deep learning; transfer learning; ensemble classification melanoma detection; deep learning; transfer learning; ensemble classification
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MDPI and ACS Style

Manzo, M.; Pellino, S. Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection. J. Imaging 2020, 6, 129. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120129

AMA Style

Manzo M, Pellino S. Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection. Journal of Imaging. 2020; 6(12):129. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120129

Chicago/Turabian Style

Manzo, Mario; Pellino, Simone. 2020. "Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection" J. Imaging 6, no. 12: 129. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120129

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