Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology.

Journal: BMC cancer
Published Date: Jan 3, 2025

Abstract

OBJECTIVE: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained cytopathologists hamper utilization of ROSE. Therefore, developing an improved deep learning model to assist clinicians in promptly and accurately evaluating Diff-Quik stained cytology samples during ROSE has important clinical value.

Authors

  • Wei Gong
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China.
  • Deep K Vaishnani
    School of International Studies, Wenzhou Medical University, Wenzhou, Zhejiang 325035, China.
  • Xuan-Chen Jin
    School of Clinical Medicine, Wenzhou Medical University, Ouhai District, Chashan, Wenzhou, Zhejiang Province, 325035, China.
  • Jing Zeng
    Department of Pharmacy, the Second Xiangya Hospital, Central South University, NO139, Renmin Road, Changsha, Hunan 410011, China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Huixia Huang
    Department of Archives, Lishui Second People's Hospital, Liandu District, Lishui City, 323000, Zhejiang Province, China.
  • Yu-Qing Zhou
    School of International Studies, Wenzhou Medical University, Ouhai District, Chashan, Wenzhou, 325035, Zhejiang Province, China.
  • Khaing Wut Yi Hla
    School of International Studies, Wenzhou Medical University, Ouhai District, Chashan, Wenzhou, 325035, Zhejiang Province, China.
  • Chen Geng
    Academy for Engineering and Technology, Fudan University, Shanghai, China.
  • Jun Ma
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.

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