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LoID-EEC: Localizing and Identifying Early Esophageal Cancer Based on Deep Learning in Screening Chromoendoscopy

Published: 29 December 2018 Publication History

Abstract

Esophageal cancer is one of the most common malignant tumors which responses for about 400,000 deaths each year. Early identifying lesions is critical for reducing esophageal cancer mortality and the overall esophageal cancer burden. However, identification of early esophageal cancerous lesions can be very challenging for clinicians owing to the mild clinical symptoms and lack of specificity of esophageal cancer. Consequently, precancer or subtle early neoplastic changes may not be evident, limiting the diagnostic accuracy. As a clinical assistance for early esophageal cancer identification, a deep learning framework referred to as the M-Deeplab model was proposed for the localization and recognition of esophageal mucosa lesion. The proposed M-Deeplab model was extended from the Deeplabv3+ model by employing an encoder-decoder structure for accuracy improvement. It achieves high-precision semantic segmentation for different staining degrees and different sizes of endoscopic images. The overall accuracy reaches 97.31% and the MIoU reaches 92.09%. Moreover, it takes only 0.05s to judge one image by the M-Deeplab model. The M-Deeplab model exhibits good performance both in accuracy and speed for early esophageal cancerous lesions identification, comparable to the experienced clinicians. As an assistance for the clinicians, the proposed model could possibly increase the early esophageal cancer diagnosis accuracy and decrease the misdiagnosis.

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Cited By

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  • (2022)Three feature streams based on a convolutional neural network for early esophageal cancer identificationMultimedia Tools and Applications10.1007/s11042-022-13135-081:26(38001-38018)Online publication date: 1-Nov-2022
  • (2020)Depth Information-Based Automatic Annotation of Early Esophageal Cancers in Gastroscopic Images Using Deep Learning TechniquesIEEE Access10.1109/ACCESS.2020.29966318(97907-97919)Online publication date: 2020
  • (2019)Localizing and Identifying Intestinal Metaplasia Based on Deep Learning in Oesophagoscope2019 8th International Symposium on Next Generation Electronics (ISNE)10.1109/ISNE.2019.8896546(1-4)Online publication date: Oct-2019

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  1. LoID-EEC: Localizing and Identifying Early Esophageal Cancer Based on Deep Learning in Screening Chromoendoscopy

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    cover image ACM Other conferences
    ICVIP '18: Proceedings of the 2018 2nd International Conference on Video and Image Processing
    December 2018
    252 pages
    ISBN:9781450366137
    DOI:10.1145/3301506
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • Kyoto University: Kyoto University
    • TU: Tianjin University

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    New York, NY, United States

    Publication History

    Published: 29 December 2018

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    Author Tags

    1. Deep Learning
    2. Early Esophageal Cancer
    3. Endoscopy
    4. Semantic Segmentation

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    Cited By

    View all
    • (2022)Three feature streams based on a convolutional neural network for early esophageal cancer identificationMultimedia Tools and Applications10.1007/s11042-022-13135-081:26(38001-38018)Online publication date: 1-Nov-2022
    • (2020)Depth Information-Based Automatic Annotation of Early Esophageal Cancers in Gastroscopic Images Using Deep Learning TechniquesIEEE Access10.1109/ACCESS.2020.29966318(97907-97919)Online publication date: 2020
    • (2019)Localizing and Identifying Intestinal Metaplasia Based on Deep Learning in Oesophagoscope2019 8th International Symposium on Next Generation Electronics (ISNE)10.1109/ISNE.2019.8896546(1-4)Online publication date: Oct-2019

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