Light Bladder Net: Non-invasive Bladder Cancer Prediction by Weighted Deep Learning Approaches and Graphical Data Transformation.

Journal: Anticancer research
PMID: 40295062

Abstract

BACKGROUND/AIM: Bladder cancer (BCa) is associated with high recurrence rates, emphasizing the importance of early and accurate detection. This study aimed to develop a lightweight and fast deep learning model, Light-Bladder-Net (LBN), for non-invasive BCa detection using conventional urine data.

Authors

  • Chi-Hua Tung
    Department of Bioinformatics, Chung-Hua University, Hsinchu, Taiwan (R.O.C.).
  • Shih-Huan Lin
    Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Taichung, Taiwan, R.O.C.
  • Kai-Po Chang
    Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Taichung, 402, Taiwan.
  • Ya-Wen Xu
    Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan, R.O.C.
  • Min-Ling Chuang
    Department of Pathology, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung, Taiwan, R.O.C.
  • Yen-Wei Chu
    Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan.

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