Home ›
Entities
› academia
› Accurate diabetic retinopathy segmentation and classification model using gated recurrent unit with residual attention network
Accurate diabetic retinopathy segmentation and classification model using gated recurrent unit with residual attention network
Research article (Biomedical Signal Processing and Control, 2024) · cited 11× · AI/ML
Accurate diabetic retinopathy segmentation and classification model using gated recurrent unit with residual attention network
Summary
Accurate diabetic retinopathy segmentation and classification model using gated recurrent unit with residual attention network is a scholarly article[1].
Key Facts
Accurate diabetic retinopathy segmentation and classification model using gated recurrent unit with residual attention network's instance of is recorded as scholarly article[2].
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Accurate diabetic retinopathy segmentation and classification model using gated recurrent unit with residual attention network. Retrieved May 24, 2026, from https://4ort.xyz/entity/accurate-diabetic-retinopathy-segmentation-and-classification-model-using-gated-recurrent-unit-with-residual-attention-n
MLA“Accurate diabetic retinopathy segmentation and classification model using gated recurrent unit with residual attention network.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/accurate-diabetic-retinopathy-segmentation-and-classification-model-using-gated-recurrent-unit-with-residual-attention-n.
BibTeX@misc{4ortxyz_accurate-diabetic-retinopathy-segmentation-and-classification-model-using-gated-recurrent-unit-with-residual-attention-n_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Accurate diabetic retinopathy segmentation and classification model using gated recurrent unit with residual attention network}}, year = {2026}, url = {https://4ort.xyz/entity/accurate-diabetic-retinopathy-segmentation-and-classification-model-using-gated-recurrent-unit-with-residual-attention-n}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Accurate diabetic retinopathy segmentation and classification model using gated recurrent unit with residual attention network — https://4ort.xyz/entity/accurate-diabetic-retinopathy-segmentation-and-classification-model-using-gated-recurrent-unit-with-residual-attention-n (retrieved 2026-05-24)
