Diabetic and hypertensive retinopathy screening in fundus images using artificially intelligent shallow architectures

Muhammad Arsalan, Adnan Haider, Jiho Choi, Kang Ryoung Park

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Retinal blood vessels are considered valuable biomarkers for the detection of diabetic retinopathy, hypertensive retinopathy, and other retinal disorders. Ophthalmologists analyze retinal vasculature by manual segmentation, which is a tedious task. Numerous studies have focused on automatic retinal vasculature segmentation using different methods for ophthalmic disease analysis. However, most of these methods are computationally expensive and lack robustness. This paper proposes two new shallow deep learning architectures: dual-stream fusion network (DSF-Net) and dual-stream aggregation network (DSA-Net) to accurately detect retinal vasculature. The proposed method uses semantic segmentation in raw color fundus images for the screening of diabetic and hypertensive retinopathies. The proposed method’s performance is assessed using three publicly available fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of Retina (STARE), and Children Heart Health Study in England Database (CHASE-DB1). The experimental results revealed that the proposed method provided superior segmentation performance with accuracy (Acc), sensitivity (SE), specificity (SP), and area under the curve (AUC) of 96.93%, 82.68%, 98.30%, and 98.42% for DRIVE, 97.25%, 82.22%, 98.38%, and 98.15% for CHASE-DB1, and 97.00%, 86.07%, 98.00%, and 98.65% for STARE datasets, respectively. The experimental results also show that the proposed DSA-Net provides higher SE compared to the existing approaches. It means that the proposed method detected the minor vessels and provided the least false negatives, which is extremely important for diagnosis. The proposed method provides an automatic and accurate segmentation mask that can be used to highlight the vessel pixels. This detected vasculature can be utilized to compute the ratio between the vessel and the non-vessel pixels and distinguish between diabetic and hypertensive retinopathies, and morphology can be analyzed for related retinal disorders.

Original languageEnglish
Article number7
JournalJournal of Personalized Medicine
Volume12
Issue number1
DOIs
StatePublished - Jan 2022

Keywords

  • Diabetic retinopathy
  • Fundus images
  • Hypertensive retinopathy
  • Ophthalmic diseases
  • Retinal disease screening
  • Retinal vasculature

Fingerprint

Dive into the research topics of 'Diabetic and hypertensive retinopathy screening in fundus images using artificially intelligent shallow architectures'. Together they form a unique fingerprint.

Cite this