AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It is demonstrated that anatomically guided feature extraction can provide accurate, efficient, and interpretable OCT disease classification, offering a practical alternative to less transparent end-to-end deep learning approaches.
A systematic comparative evaluation of three deep learning-based segmentation architectures — U-Net, U-Net++, and Y-Net — for automated identification of DME and Intraretinal Fluid regions in OCT scans demonstrates the feasibility of deep learning-based OCT segmentation as a diagnostic support tool in resource-constrai...
A lightweight TUnet+ network incorporating an innovatively designed PSE_C2fCIB module provides a robust technical foundation for automated diagnostic support and quantitative disease assessment, and it demonstrates the value of electromagnetic-wave imaging analysis in biomedical engineering.
Timely and precise detection of retinal diseases is essential for preventing permanent vision loss. However, the analysis of Optical Coherence Tomography (OCT) images is often time-taking and subject to inter-clinician variability. This paper presents deep learning architectures for multi-disease classification of OCT...
Accurate localization and segmentation of the optic disc (OD) are important for retinal image analysis and glaucoma assessment, yet remain challenging due to variations in illumination, pathology, vascular interference, and poorly defined boundaries. This structured methodological review examines the evolution of OD se...
Optical coherence tomography (OCT) is a high-resolution and non-contact optical imaging and sensing modality that provides depth-resolved cross-sectional visualization of retinal microstructures. It plays an important role in the assessment of retinal diseases, including age-related macular degeneration (AMD) and diabe...
The retinal image analysis is important in the early diagnosis of ocular conditions like diabetic retinopathy, glaucoma, and hypertension. The diagnostics of retinal blood vessels is tedious, and also requires professional skills to be identified by hand, which is why automated options are highly appreciated. An adapte...
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