The publisher or authors have withdrawn this paper. Cite with extreme caution; check the publisher's notice for the reason.
Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification
Hainan Normal University · Air University · +4 more institutions
Abstract
The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach. Addressing this, our study introduces a novel methodology that amplifies the robustness and precision of the detection process. This is complemented by the groundbreaking Hierarchical Block Attention (HBA) and HBA-U-Net architecture, which notably propel attention mechanisms in…
Citation impact
- FWCI
- 46.82
- Percentile
- 100%
- References
- 44
Authors
7- ABAnas BilalCorresponding
Hainan Normal University
- AIAzhar ImranCorresponding
Air University
- TITalha Imtiaz BaigCorresponding
University of Electronic Science and Technology of China, University of Management and Technology
- XLXiaowen LiuCorresponding
Hainan Normal University
- HLHaixia LongCorresponding
Hainan Normal University
Topics & keywords
- Diabetic retinopathy
- Support vector machine
- Artificial intelligence
- Pattern recognition (psychology)
- Computer science
- Retinopathy
- Medicine
- Ophthalmology
- Industry, innovation and infrastructure