The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Fusing fine-tuned deep features for skin lesion classification
Open accessAbstract
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.
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Cited by
Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification
Mahbod, A., Schaefer, G., Wang, C., Dorffner, G., Ecker, R., & Ellinger, I. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105475 · Cited by 312
Self-citationAnalysis of the ISIC image datasets: Usage, benchmarks and recommendations
Cassidy, B., Kendrick, C., Brodzicki, A., Jaworek-Korjakowska, J., & Yap, M. H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102305 · Cited by 307
A multi-class skin Cancer classification using deep convolutional neural networks
Chaturvedi, S. S., Tembhurne, J. V., & Diwan, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09388-2 · Cited by 283
Khan, M. A., Sharif, M., Akram, T., Damaševičius, R., & Maskeliūnas, R. (2021) · Diagnostics · doi:10.3390/diagnostics11050811 · Cited by 281
Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., Beltraminelli, H., Berking, C., Betz-Stablein, B., Blum, A., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.06.049 · Cited by 267
Metadata
- Authors
- Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Pitiot, A., & Wang, C.
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 71
- Pages
- 19-29
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W2899425762
- Cited by
- 264
- References
- 50
- FWCI
- 6.74
- Type
- journal article
- Language
- en
- Open access
- green
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- Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Pitiot, A., & Wang, C. (2019). Fusing fine-tuned deep features for skin lesion classification. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2018.10.007
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