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

  • Published online on: September 27, 2019     https://doi.org/10.3892/ol.2019.10928
  • Copyright: © Sena et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


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.

Related Articles

Journal Cover

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Sena, P., Fioresi, R., Faglioni, F., Losi, L., Faglioni, G., & Roncucci, L. (1899). Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images. Oncology Letters, 0, 0-0. 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 0.0 (1899): 0-0.
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 0, no. 0 (1899): 0-0. https://doi.org/10.3892/ol.2019.10928