Rethinking Skin Lesion Segmentation in a Convolutional Classifier

Journal of Digital Imaging volume 31, pages435–440(2018)Cite this article

Abstract

Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can improve diagnostic accuracy and save lives. CNNs have been successfully used in both skin lesion segmentation and classification. For reasons heretofore unclear, previous works have found image segmentation to be, conflictingly, both detrimental and beneficial to skin lesion classification. We investigate the effect of expanding the segmentation border to include pixels surrounding the target lesion. Ostensibly, segmenting a target skin lesion will remove inessential information, non-lesion skin, and artifacts to aid in classification. Our results indicate that segmentation border enlargement produces, to a certain degree, better results across all metrics of interest when using a convolutional based classifier built using the transfer learning paradigm. Consequently, preprocessing methods which produce borders larger than the actual lesion can potentially improve classifier performance, more than both perfect segmentation, using dermatologist created ground truth masks, and no segmentation altogether.

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Acknowledgements

The authors gratefully acknowledge funding from NSF Award No. 1464537, Industry/University Cooperative Research Center, Phase II under NSF 13-542. We are also thankful to the 33 corporations that are the members of the Center for their active participation and funding.

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Affiliations

  1. Florida Atlantic University, Boca Raton, FL, USA

    Jack Burdick, Oge Marques, Janet Weinthal & Borko Furht

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  1. Jack Burdick
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  2. Oge Marques
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  3. Janet Weinthal
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  4. Borko Furht
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Correspondence to Oge Marques.

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Burdick, J., Marques, O., Weinthal, J. et al. Rethinking Skin Lesion Segmentation in a Convolutional Classifier. J Digit Imaging 31, 435–440 (2018). https://doi.org/10.1007/s10278-017-0026-y

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Keywords

  • Medical decision support systems
  • Deep learning
  • Medical image analysis
  • Convolutional neural networks
  • Skin lesions
  • Machine learning