Recent trends and advances in fundus image analysis: A review

Shahzaib Iqbal, Tariq M. Khan*, Khuram Naveed, Syed S. Naqvi, Syed Junaid Nawaz

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperReviewResearchpeer-review


Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.

Original languageEnglish
Article number106277
JournalComputers in Biology and Medicine
Publication statusPublished - Dec 2022


  • Classification
  • Diabetic retinopathy
  • Eye diseases
  • Hypertensive retinopathy
  • Retinal fundus images
  • Segmentation


Dive into the research topics of 'Recent trends and advances in fundus image analysis: A review'. Together they form a unique fingerprint.

Cite this