Automated classification of panoramic radiographs with inflammatory periapical lesions using a CNN-LSTM architecture.

Journal: Journal of dentistry
PMID: 40101853

Abstract

OBJECTIVES: Considering Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network approaches have shown promising image classification performance, the aim of this study was to compare the performance of novel Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) architectures with a classic CNN for classification of panoramic radiographs with inflammatory periapical lesions.

Authors

  • Jonas Ver Berne
    Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven 3000, Belgium; OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Belgium.
  • Soroush Baseri Saadi
    OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Belgium.
  • Nicolly Oliveira-Santos
    OMFS IMPATH Research Group, Department of Imaging and Pathology, KU Leuven and University Hospitals Leuven, UZ Campus St Rafael, Leuven, Belgium.
  • Luiz Eduardo Marinho-Vieira
    OMFS-IMPATH Research Group, Department of Imaging & Pathology, Catholic University Leuven, Belgium; Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Brazil.
  • Reinhilde Jacobs
    OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 7, 3000, Leuven, Belgium; Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden. Electronic address: reinhilde.jacobs@ki.se.

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