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Polyp-Net: A Multimodel Fusion Network for Polyp Segmentation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50017375" target="_blank" >RIV/62690094:18450/21:50017375 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9163355" target="_blank" >https://ieeexplore.ieee.org/document/9163355</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/TIM.2020.3015607" target="_blank" >10.1109/TIM.2020.3015607</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Polyp-Net: A Multimodel Fusion Network for Polyp Segmentation

  • Original language description

    Computer-aided diagnosis of disease primarily depends on proper vision-based measurement (VBM). The traditional approach followed for diagnosis of colorectal cancer includes a manual screening of colorectum via a colonoscope and resection of polyps for histopathological analysis to decide the grade of malignancy. This procedure is time-consuming and expensive, and removal of benign polyp for analysis signifies the inefficiency of the diagnosis system. These drawbacks inspired us to develop an automatic vision-based analysis method for preliminary in vivo malignancy analysis of the polyp region. In this work, we have proposed a fusion-based polyp segmentation network, namely, Polyp-Net. Recently, convolutional neural networks (CNNs) have shown immense success in the domain of medical image analysis as it can exploit in-depth significant features with high discrimination capabilities. Therefore, motivated by these insights, we have proposed an enriched version of CNN with a nascent pooling mechanism, namely dual-tree wavelet pooled CNN (DT-WpCNN). The resultant segmented mask contains some surplus high-intensity regions apart from the polyp region. These shortcomings are avoided using a new variation of the region-based level-set method, namely, the local gradient weighting-embedded level-set method (LGWe-LSM), which shows a significant reduction of false-positive rate. The pixel-level fusion of the two enhanced methods shows more potentiality in the segmentation of the polyp regions. Our proposed network is trained on CVC-colon DB and tested on CVC-clinic DB. It achieves a dice score of 0.839, volume-similarity of 0.863, precision of 0.836, recall of 0.811, F1-score of 0.823, F2-score of 0.815, and Hausdorff distance of 21.796 which outperforms the existing baseline CNN&apos;s and recent state-of-the-art methods. © 1963-2012 IEEE.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10308 - Astronomy (including astrophysics,space science)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE Transactions on Instrumentation and Measurement

  • ISSN

    0018-9456

  • e-ISSN

  • Volume of the periodical

    70

  • Issue of the periodical within the volume

    Winter

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    12

  • Pages from-to

    "Article Number: 4000512"

  • UT code for WoS article

    000591842200012

  • EID of the result in the Scopus database

    2-s2.0-85096669103