Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations.

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

Abstract

BACKGROUND: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking.

Authors

  • Dildar Hussain
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.
  • Mohammed A Al-Masni
    Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Republic of Korea.
  • Muhammad Aslam
    Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Defense Road, Off Raiwind Road, Lahore, Pakistan.
  • Abolghasem Sadeghi-Niaraki
    Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, 19697, Tehran, Iran. a.sadeghi.ni@gmail.com.
  • Jamil Hussain
    Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. jamil@oslab.khu.ac.kr.
  • Yeong Hyeon Gu
    Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea.
  • Rizwan Ali Naqvi
    Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 100-715, Korea. rizwanali@dongguk.edu.

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