A user-friendly deep learning application for accurate lung cancer diagnosis.

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2024

Abstract

BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis.

Authors

  • Duong Thanh Tai
    Department of Medical Physics, Faculty of Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam.
  • Nguyen Tan Nhu
    School of Biomedical Engineering, Ho Chi Minh City International University (VNU-HCM), Ho Chi Minh City, Vietnam.
  • Pham Anh Tuan
    Nuclear Medicine and Oncology Centre, Bach Mai Hospital, Ha Noi, Vietnam.
  • Abdelmoneim Sulieman
    Radiology and Medical Imaging Department Prince Sattam Bin Abdulaziz University College of Applied Medical Sciences, Al-Kharj, Saudi Arabia.
  • Hiba Omer
    Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
  • Zahra Alirezaei
    Radiology Department, Paramedical School, Bushehr University of Medical Sciences, Bushehr, Iran.
  • David Bradley
    Applied Physics and Radiation Technologies Group, CCDCU, Sunway University, Subang Jaya, PJ, Malaysia.
  • James C L Chow
    Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.

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