YOLOを用いた複数の病変学習によるカプセル内視鏡画像の病変候補検出

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/09/01

タイトル(英語): Detection of Multiple Lesion Candidates on Capsule Endoscopy Images by Learning Multiple Lesions using YOLOv5

著者名: 伊東　樹（山梨大学），小谷　信司（山梨大学），渡辺　寛望（山梨大学）

著者名(英語): Tatsuki Ito (University of Yamanashi), Shinji Kotani (University of Yamanashi), Hiromi Watanabe (University of Yamanashi)

キーワード: カプセル内視鏡，YOLOv5，病変候補検出　　capsule endoscope，YOLOv5，detection of lesion candidates

要約(英語): The Capsule endoscopy is a technique to capture images of the inside of the gastrointestinal tract by swallowing a device measuring approximately 11 mm in diameter and 26 mm in length. Compared with conventional endoscopy, capsule endoscopy is less burdensome on patients while allowing observations of the small intestine. This non-invasive technique produces more than 50,000 images in a single examination. Because a physician must visually check each image, a diagnosis is time consuming and labor intensive. This study investigated automatic detection of lesions to reduce the burden on physicians, preventing missed lesions and support diagnosis.Here, we use YOLOv5 (You Only Look Once version 5), which is a general object detection model, to automatically detect lesions after training a model with 3 types of lesion images. When the recall was 100% to ensure that no lesion was missed, the polyp accuracy, ulcer accuracy, Type A accuracy and Type B accuracy were 96%, 99%, 77% and 94%, respectively. In the future, we will train the model with additional images of other lesions and improve the precision rate.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.9 （2023） 特集：知能メカトロニクス分野と連携する知覚情報技術

本誌掲載ページ: 901-908 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/9/143_901/_article/-char/ja/