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Improving the performance of convolutional neural network for skin image classification using the response of image analysis filters

  • S.I. : EANN 2017
  • Published:
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Abstract

In this work, we focus in the analysis of dermoscopy images using convolutional neural networks (CNNs). More specifically, we investigate the value of augmenting CNN inputs with the response of mid-level computer vision filters, using the traditional inputting of simple RGB pixel values as baseline. The proposed methodology is applied on two pattern recognition problems with clinical significance: the binary classification of skin lesions in dermoscopy images into “malignant” and “non-malignant” (nevus skin lesions) cases and the four-class, superpixel classification into differential structures that appear in skin lesions. The transfer learning technique is also utilized to compensate for the limited size of the available training image datasets. Results show that filter-based input augmentation using the response of mid-level computer vision filters significantly improves the classification accuracy achieved by the CNN architectures and simplifies the weights of the receptive fields.

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Acknowledgments

We gratefully acknowledge the support of NVIDIA Corporation for the donation of the Titan X Pascal GPU used for this research.

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Authors and Affiliations

  1. Department of Computer Science and Biomedical Informatics, University of Thessaly, Papassiopoulou 2-4, Lamia, Greece

    S. V. Georgakopoulos, K. Kottari, K. Delibasis & V. P. Plagianakos

  2. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    I. Maglogiannis

Authors
  1. S. V. Georgakopoulos
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  2. K. Kottari
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  3. K. Delibasis
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  4. V. P. Plagianakos
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  5. I. Maglogiannis
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Correspondence to I. Maglogiannis.

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Georgakopoulos, S.V., Kottari, K., Delibasis, K. et al. Improving the performance of convolutional neural network for skin image classification using the response of image analysis filters. Neural Comput & Applic 31, 1805–1822 (2019). https://doi.org/10.1007/s00521-018-3711-y

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  • DOI: https://doi.org/10.1007/s00521-018-3711-y

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