Severity grading of hypertensive retinopathy using hybrid deep learning architecture.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVES: Hypertensive Retinopathy (HR) is a retinal manifestation resulting from persistently elevated blood pressure. Severity grading of HR is essential for patient risk stratification, effective management, progression monitoring, timely intervention, and minimizing the risk of vision impairment. Computer-aided diagnosis and artificial intelligence (AI) systems play vital roles in the diagnosis and grading of HR. Over the years, very limited research has been conducted for the grading of HR. Nevertheless, there are no publicly available datasets for HR grading. Moreover, one of the key challenges observed is high-class imbalance.

Authors

  • Supriya Suman
    Interdisciplinary Research Division: Smart Healthcare, Indian Institute of Technology, Jodhpur, 342030, Rajasthan, India. Electronic address: suman.4@iitj.ac.in.
  • Anil Kumar Tiwari
    Department of Electrical Engineering, Indian Institute of Technology Jodhpur, 342030, Rajasthan, India.
  • Shreya Sachan
    Department of Electrical Engineering, Indian Institute of Technology Jodhpur, 342030, Rajasthan, India.
  • Kuldeep Singh
    Department of Electronics Technology, Guru Nanak Dev University, Amritsar, India. kuldeep.ece@gndu.ac.in.
  • Seema Meena
    Department of Ophthalmology, All India Institute of Medical Sciences, Jodhpur, 342005, Rajasthan, India.
  • Sakshi Kumar
    Regional Institute of Ophthalmology, Indira Gandhi Institute of Medical Sciences, Patna, 800025, Bihar, India.