A novel transfer learning approach for the classification of histological images of colorectal cancer

Abstract

Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification of eight types of tissues found in CRC histopathological evaluation. We apply Transfer Learning from architectures of Convolutional Neural Networks (CNNs). We modify the structures of CNNs to extract features from the images and input them to well-known machine learning methods: Naive Bayes, Multilayer Perceptron, k-Nearest Neighbors, Random Forest, and Support Vector Machine (SVM). We evaluated 108 extractor–classifier combinations. The one that achieved the best results is DenseNet169 with SVM (RBF), reaching an Accuracy of 92.083% and F1-Score of 92.117%. Therefore, our approach is capable of distinguishing tissues found in CRC histopathological evaluation.

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Notes

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    We considered the SVM classifier with different kernels as different classifiers.

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Acknowledgements

The authors are grateful to the Deanship of Scientific Research at King Saud University for funding this work through Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing. This study was also supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Also, Pedro Pedrosa Rebouças Filho acknowledges the sponsorship from the Brazilian National Council for Research and Development (CNPq) via Grants Nos. 431709/2018-1 and 311973/2018-3.

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Correspondence to Victor Hugo Costa de Albuquerque.

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Ohata, E.F., Chagas, J.V.S.d., Bezerra, G.M. et al. A novel transfer learning approach for the classification of histological images of colorectal cancer. J Supercomput (2021). https://doi.org/10.1007/s11227-020-03575-6

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Keywords

  • Transfer learning
  • Colorectal cancer
  • Histological images
  • Convolutional neural networks