Pith. sign in

Paper Citation Record · LEDGER

Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2207.03342.

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

pith.paper-citation-record.v1
2207.03342 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:41:21.109389Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:27:56.803796Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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 30032076-23f3-421c-a2dc-bdeae152f58e · inbound

A Cascaded Dilated Convolution Approach for Mpox Lesion Classification cites this paper.

A Cascaded Dilated Convolution Approach for Mpox Lesion Classification Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-11T16:24:14.901074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:24:14.901074Z digest=sha256:1be7247719460544f8e6e0fcd4368fe4ec01c2fa7a5e358a0c4b98731a67227b

Observation f2c9f252-650a-4a26-b588-e496f6740b45 · inbound

Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging cites this paper.

Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-10T23:40:44.644238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:40:44.644238Z digest=sha256:4f2b5a394b54e9f866273a331ff65cd11e93373cfd5c9275d627b53a59f9a6a7

Observation fee00e1b-474a-42b4-9e3e-6194732c209f · inbound

An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm cites this paper.

An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-16T10:41:21.109389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T10:41:21.109389Z digest=sha256:77bb65e787bb5130fe76137001991edc0acf9479fbd33641bd2f5de34ae24022

Observation 943ad315-c66f-4ed3-9f1b-f39089c7060e · inbound

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique cites this paper.

An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-08-06T15:27:56.807518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T15:27:56.618768Z digest=sha256:3f8e379b199d1fa018511abe4bfa21f0a44c8c931affa0d1401e84348771ef8c