Automated classification of multiple ophthalmic diseases using ultrasound images by deep learning.

Journal: The British journal of ophthalmology
Published Date: Jun 20, 2024

Abstract

BACKGROUND: Ultrasound imaging is suitable for detecting and diagnosing ophthalmic abnormalities. However, a shortage of experienced sonographers and ophthalmologists remains a problem. This study aims to develop a multibranch transformer network (MBT-Net) for the automated classification of multiple ophthalmic diseases using B-mode ultrasound images.

Authors

  • Yijie Wang
    College of Computer, National University of Defense Technology, Changsha 410073, China.
  • Zihao Xu
  • Ruilong Dan
    Microelectronics CAD Center, Hangzhou Dianzi University, Hangzhou, China.
  • Chunlei Yao
    Department of Ophthalmology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Ji Shao
    Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China.
  • Yiming Sun
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People's Republic of China.
  • Yaqi Wang
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Juan Ye
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

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