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Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings

Research Article

Automating the Annotation of Medical Images in Capsule Endoscopy Through Convolutional Neural Networks and CBIR

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-60665-6_15,
        author={Rodrigo Fernandes and Marta Salgado and Ishak Pa\`{e}al and Ant\^{o}nio Cunha},
        title={Automating the Annotation of Medical Images in Capsule Endoscopy Through Convolutional Neural Networks and CBIR},
        proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2024},
        month={6},
        keywords={Automatic Medical Image Annotation Convolutional Neural Networks Content-Based Image Retrieval},
        doi={10.1007/978-3-031-60665-6_15}
    }
    
  • Rodrigo Fernandes
    Marta Salgado
    Ishak Paçal
    António Cunha
    Year: 2024
    Automating the Annotation of Medical Images in Capsule Endoscopy Through Convolutional Neural Networks and CBIR
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-031-60665-6_15
Rodrigo Fernandes1,*, Marta Salgado2, Ishak Paçal, António Cunha1
  • 1: UTAD—University of Trás-os-Montes and Alto Douro
  • 2: Centro Hospitalar Universitário de Santo António
*Contact email: rodrigo.c.fernandes@inesctec.pt

Abstract

This research addresses the significant challenge of automating the annotation of medical images, with a focus on capsule endoscopy videos. The study introduces a novel approach that synergistically combines Deep Learning and Content-Based Image Retrieval (CBIR) techniques to streamline the annotation process. Two pre-trained Convolutional Neural Networks (CNNs), MobileNet and VGG16, were employed to extract and compare visual features from medical images. The methodology underwent rigorous validation using various performance metrics such as accuracy, AUC, precision, and recall. The MobileNet model demonstrated exceptional performance with a test accuracy of 98.4%, an AUC of 99.9%, a precision of 98.2%, and a recall of 98.6%.

On the other hand, the VGG16 model achieved a test accuracy of 95.4%, an AUC of 99.2%, a precision of 97.3%, and a recall of 93.5%. These results indicate the high efficacy of the proposed method in the automated annotation of medical images, establishing it as a promising tool for medical applications. The study also highlights potential avenues for future research, including expanding the image retrieval scope to encompass entire endoscopy video databases.

Keywords
Automatic Medical Image Annotation Convolutional Neural Networks Content-Based Image Retrieval
Published
2024-06-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-60665-6_15
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