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Article Open Access

Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images

  • Authors:
    • Paola Sena
    • Rita Fioresi
    • Francesco Faglioni
    • Lorena Losi
    • Giovanni Faglioni
    • Luca Roncucci
  • View Affiliations / Copyright

    Affiliations: Department of Biomedical, Metabolic and Neurosciences, University of Modena and Reggio Emilia, I-41125 Modena, Italy, Department of Mathematics, University of Bologna, I-40126 Bologna, Italy, Department of Chemistry and Geology, University of Modena and Reggio Emiliaa, I-41125 Modena, Italy, Department of Life Sciences, University of Modena and Reggio Emiliaa, I-41125 Modena, Italy, Nabla 2, Srl, I-41124 Modena, Italy, Department of Diagnostic and Clinical Medicine, and Public Health, University of Modena and Reggio Emilia, I-41125 Modena, Italy
    Copyright: © Sena et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 6101-6107
    |
    Published online on: September 27, 2019
       https://doi.org/10.3892/ol.2019.10928
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Abstract

Trained pathologists base colorectal cancer identification on the visual interpretation of microscope images. However, image labeling is not always straightforward and this repetitive task is prone to mistakes due to human distraction. Significant efforts are underway to develop informative tools to assist pathologists and decrease the burden and frequency of errors. The present study proposes a deep learning approach to recognize four different stages of cancerous tissue development, including normal mucosa, early preneoplastic lesion, adenoma and cancer. A dataset of human colon tissue images collected and labeled over a 10‑year period by a team of pathologists was partitioned into three sets. These were used to train, validate and test the neural network, comprising several convolutional and a few linear layers. The approach used in the present study is ‘direct’; it labels raw images and bypasses the segmentation step. An overall accuracy of >95% was achieved, with the majority of mislabeling referring to a near category. Tests on an external dataset with a different resolution yielded accuracies >80%. The present study demonstrated that the neural network, when properly trained, can provide fast, accurate and reproducible labeling for colon cancer images, with the potential to significantly improve the quality and speed of medical diagnoses.
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Copy and paste a formatted citation
Spandidos Publications style
Sena P, Fioresi R, Faglioni F, Losi L, Faglioni G and Roncucci L: Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images. Oncol Lett 18: 6101-6107, 2019.
APA
Sena, P., Fioresi, R., Faglioni, F., Losi, L., Faglioni, G., & Roncucci, L. (2019). Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images. Oncology Letters, 18, 6101-6107. https://doi.org/10.3892/ol.2019.10928
MLA
Sena, P., Fioresi, R., Faglioni, F., Losi, L., Faglioni, G., Roncucci, L."Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images". Oncology Letters 18.6 (2019): 6101-6107.
Chicago
Sena, P., Fioresi, R., Faglioni, F., Losi, L., Faglioni, G., Roncucci, L."Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images". Oncology Letters 18, no. 6 (2019): 6101-6107. https://doi.org/10.3892/ol.2019.10928
Copy and paste a formatted citation
x
Spandidos Publications style
Sena P, Fioresi R, Faglioni F, Losi L, Faglioni G and Roncucci L: Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images. Oncol Lett 18: 6101-6107, 2019.
APA
Sena, P., Fioresi, R., Faglioni, F., Losi, L., Faglioni, G., & Roncucci, L. (2019). Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images. Oncology Letters, 18, 6101-6107. https://doi.org/10.3892/ol.2019.10928
MLA
Sena, P., Fioresi, R., Faglioni, F., Losi, L., Faglioni, G., Roncucci, L."Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images". Oncology Letters 18.6 (2019): 6101-6107.
Chicago
Sena, P., Fioresi, R., Faglioni, F., Losi, L., Faglioni, G., Roncucci, L."Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images". Oncology Letters 18, no. 6 (2019): 6101-6107. https://doi.org/10.3892/ol.2019.10928
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