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Paper Citation Record · LEDGER

Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.07338.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2405.07338 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:10:55.579346Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T08:16:48.339665Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation fbbce8ba-b89f-463a-9a02-482173c48f7a · inbound

An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI cites this paper.

An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T15:09:58.028346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:09:58.028346Z digest=sha256:f39b372bb77bc59776c119473e61fe16331804275a77df9c928e5515bf3bc5cf

Observation 78d32d7f-0a73-4521-b37e-219122d321a1 · inbound

An Exploratory Approach Towards Investigating and Explaining Vision Transformer and Transfer Learning for Brain Disease Detection cites this paper.

An Exploratory Approach Towards Investigating and Explaining Vision Transformer and Transfer Learning for Brain Disease Detection Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T15:10:55.579346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:10:55.579346Z digest=sha256:6461739331d6580d95e815a2e38ac65c2635c8834b9a9c6d1c36f57c37d5bb1e

Observation 1df9f840-62db-41be-87f3-cdf0cd352741 · inbound

Lightweight Convolutional Neural Networks for Retinal Disease Classification cites this paper.

Lightweight Convolutional Neural Networks for Retinal Disease Classification Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T12:21:35.552986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:21:35.552986Z digest=sha256:54b366ef37163290a0ddbde973ecf5b85a71627e44b1f6db15a80da5cbfaaa5d

Observation b570c8fd-45ae-48ee-ac56-bd19df61d967 · inbound

Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh cites this paper.

Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T05:43:12.992478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:43:12.992478Z digest=sha256:505419279e861f9495ae8b6a4f406292eb117e3b5d0aa3a3db502479b53f3be9

Observation de5283d4-8490-4384-b4b1-2d348d37abde · inbound

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation cites this paper.

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-07-02T08:16:48.341283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T06:09:38.263791Z digest=sha256:e8f6a949497671b28c221c9ced410b9c428006f08525efb3763d46dbd952a7a3