• Admin Login
    View Item 
    •   Home
    • Faculty of Science and Engineering
    • Faculty of Science and Engineering
    • View Item
    •   Home
    • Faculty of Science and Engineering
    • Faculty of Science and Engineering
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of WIRECommunitiesTitleAuthorsIssue DateSubmit DateSubjectsTypesJournalDepartmentPublisherThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsTypesJournalDepartmentPublisher

    Administrators

    Admin Login

    Local Links

    AboutThe University LibraryOpen Access Publications PolicyDeposit LicenceCOREWIRE Copyright and Reuse Information

    Statistics

    Display statistics

    Towards an interpretable model for automatic classification of endoscopy images

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Garcia-Aguirre_Towards_an_inte ...
    Embargo:
    2023-10-23
    Size:
    2.737Mb
    Format:
    PDF
    Download
    Authors
    García-Aguirre, Rogelio
    Torres Treviño, Luis
    Navarro-López, Eva María cc
    González-González, José Alberto
    Editors
    Pichardo Lagunas, Obdulia
    Martínez Seis, Bella
    Martínez-Miranda, Juan
    Issue Date
    2022-10-23
    
    Metadata
    Show full item record
    Abstract
    Deep learning strategies have become the mainstream for computer-assisted diagnosis tools development since they outperform other machine learning techniques. However, these systems can not reach their full potential since the lack of understanding of their operation and questionable generalizability provokes mistrust from the users, limiting their application. In this paper, we generate a Convolutional Neural Network (CNN) using a genetic algorithm for hyperparameter optimization. Our CNN has state-of-the-art classification performance, delivering higher evaluation metrics than other recent papers that use AI models to classify images from the same dataset. We provide visual explanations of the classifications made by our model implementing Grad-CAM and analyze the behavior of our model on misclassifications using this technique.
    Citation
    García-Aguirre, R., Torres Treviño, L., Navarro-López, E.M. and González-González, J.A. (2022) Towards an interpretable model for automatic classification of endoscopy images. Lecture Notes in Artificial Intelligence, 13612, pp. 297-307. DOI: 10.1007/978-3-031-19493-1_24
    Publisher
    Springer
    URI
    http://hdl.handle.net/2436/624897
    DOI
    10.1007/978-3-031-19493-1_24
    Additional Links
    https://link.springer.com/book/9783031194948
    Type
    Conference contribution
    Language
    en
    Description
    This is an accepted manuscript of a paper published by Springer in Lecture Notes in Artificial Intelligence on 23 October 2022. Conference paper presented at the 21st Mexican International Conference on Artificial Intelligence, 24th-29th October 2022. For re-use please see the publisher's terms and conditions.
    Series/Report no.
    Lecture Notes in Artificial Intelligence
    Lecture Notes in Computer Science
    ISSN
    0302-9743
    EISSN
    1611-3349
    ISBN
    9783031194948
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-031-19493-1_24
    Scopus Count
    Collections
    Faculty of Science and Engineering

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.