Towards more efficient ophthalmic disease classification and lesion location via convolution transformer.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 27, 2022

Abstract

OBJECTIVE: A retina optical coherence tomography (OCT) image differs from a traditional image due to its significant speckle noise, irregularity, and inconspicuous features. A conventional deep learning architecture cannot effectively improve the classification accuracy, sensitivity, and specificity of OCT images, and noisy images are not conducive to further diagnosis.  This paper proposes a novel lesion-localization convolution transformer (LLCT) method, which combines both convolution and self-attention to classify ophthalmic diseases more accurately and localize the lesions in retina OCT images.

Authors

  • Huajie Wen
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China; College of Applied Science, Shenzhen University, Shenzhen 518060, China.
  • Jian Zhao
    Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, China.
  • Shaohua Xiang
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
  • Lin Lin
    Central Laboratory, The First Affiliated Hospital of Xiamen University, Xiamen, China, zhibinli33@163.com, liusuhuan@xmu.edu.cn.
  • Chengjian Liu
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China.
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Lin An
    Bioinformatics and Genomics Program, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA.
  • Lixin Liang
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China. Electronic address: lianglixin@sztu.edu.cn.
  • Bingding Huang
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China. Electronic address: huangbingding@sztu.edu.cn.

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