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Fusing fine-tuned deep features for skin lesion classification
journal contribution
posted on 2019-01-29, 09:43 authored by Amirreza Mahbod, Gerald SchaeferGerald Schaefer, Isabella Ellinger, Rupert Ecker, Alain Pitiot, Chunliang Wang© 2018 Elsevier Ltd Malignant melanoma is one of the most aggressive forms of skin cancer. Early detection is important as it significantly improves survival rates. Consequently, accurate discrimination of malignant skin lesions from benign lesions such as seborrheic keratoses or benign nevi is crucial, while accurate computerised classification of skin lesion images is of great interest to support diagnosis. In this paper, we propose a fully automatic computerised method to classify skin lesions from dermoscopic images. Our approach is based on a novel ensemble scheme for convolutional neural networks (CNNs) that combines intra-architecture and inter-architecture network fusion. The proposed method consists of multiple sets of CNNs of different architecture that represent different feature abstraction levels. Each set of CNNs consists of a number of pre-trained networks that have identical architecture but are fine-tuned on dermoscopic skin lesion images with different settings. The deep features of each network were used to train different support vector machine classifiers. Finally, the average prediction probability classification vectors from different sets are fused to provide the final prediction. Evaluated on the 600 test images of the ISIC 2017 skin lesion classification challenge, the proposed algorithm yields an area under receiver operating characteristic curve of 87.3% for melanoma classification and an area under receiver operating characteristic curve of 95.5% for seborrheic keratosis classification, outperforming the top-ranked methods of the challenge while being simpler compared to them. The obtained results convincingly demonstrate our proposed approach to represent a reliable and robust method for feature extraction, model fusion and classification of dermoscopic skin lesion images.
Funding
This work was supported by the European Union Horizon 2020 Research and Innovation Program (”CaSR Biomedicine”, 675228).
History
School
- Science
Department
- Computer Science
Published in
Computerized Medical Imaging and GraphicsVolume
71Pages
19 - 29Citation
MAHBOD, A. ... et al., 2019. Fusing fine-tuned deep features for skin lesion classification. Computerized Medical Imaging and Graphics, 71, pp. 19-29.Publisher
© ElsevierVersion
- AM (Accepted Manuscript)
Publisher statement
This paper was accepted for publication in the journal Computerized Medical Imaging and Graphics and the definitive published version is available at https://doi.org/10.1016/j.compmedimag.2018.10.007.Publication date
2019ISSN
0895-6111eISSN
1879-0771Publisher version
Language
- en