Colour fusion effect on deep learning classification of uveal melanoma.

Journal: Eye (London, England)
PMID: 38773261

Abstract

BACKGROUND: Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma and choroidal nevi, and to evaluate the effect of colour fusion options on the classification performance.

Authors

  • Albert K Dadzie
    Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, 60607, USA.
  • Sabrina P Iddir
    Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, 60612, USA.
  • Mansour Abtahi
    Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
  • Behrouz Ebrahimi
    Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
  • David Le
    Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA.
  • Sanjay Ganesh
    Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, 60612, USA.
  • Taeyoon Son
    Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA.
  • Michael J Heiferman
    Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, 60612, USA. mheif@uic.edu.
  • Xincheng Yao
    Department of Bioengineering, 14681University of Illinois at Chicago, Chicago, IL 60607, USA.

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