Artificial Intelligence Recognition Model Using Liquid-Based Cytology Images to Discriminate Malignancy and Histological Types of Non-Small-Cell Lung Cancer.

Journal: Pathobiology : journal of immunopathology, molecular and cellular biology
PMID: 39197433

Abstract

INTRODUCTION: Artificial intelligence image recognition has applications in clinical practice. The purpose of this study was to develop an automated image classification model for lung cancer cytology using a deep learning convolutional neural network (DCNN).

Authors

  • Ryota Tanaka
    Department of Clinical Pharmacy, Oita University Hospital, 1-1 Hasama-machi, Oita, 879-5593, Japan.
  • Yukihiro Tsuboshita
    Center for Data Science Education and Research, Kyorin University, Tokyo, Japan.
  • Mitsuaki Okodo
    Department of Medical Technology, Faculty of Health Sciences, Kyorin University, Tokyo, Japan.
  • Rei Settsu
    Department of Medical Technology, Faculty of Health Sciences, Kyorin University, Tokyo, Japan.
  • Kohei Hashimoto
    Department of Thoracic Surgical Oncology, Cancer Institute Hospital of JFCR, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
  • Keisei Tachibana
    Department of Thoracic and Thyroid Surgery, Kyorin University, Tokyo, Japan.
  • Kazumasa Tanabe
    Department of Pathology, Kyorin University School of Medicine, Tokyo, Japan.
  • Koji Kishimoto
    Department of Pathology, Kyorin University School of Medicine, Tokyo, Japan.
  • Masachika Fujiwara
    Department of Pathology, Kyorin University School of Medicine, Tokyo, Japan.
  • Junji Shibahara
    Department of Pathology, Kyorin University School of Medicine, Tokyo, Japan.

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