Paper Citation Record · LEDGER

Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features

As of 16 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:1908.05730.

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

pith.paper-citation-record.v1
1908.05730 v1

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:30:49.345069Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

6 of 6 outbound references displayed

verified exact0
verified fuzzy4
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e2924d61-954d-4b1e-8465-876b4a40eb78 · outbound

This paper cites ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection,.

Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:49.441527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 70313f76-e5b7-4650-855d-245bbf181e75 · outbound

This paper cites Fully Convolutional Networks for Semantic Segmentation.

Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features Fully Convolutional Networks for Semantic Segmentation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:49.338119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cc804d5-4d5e-4e19-bed5-6107a2605a16 · outbound

This paper cites 3 illustrates our proposed classification approach.

Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features 3 illustrates our proposed classification approach

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:49.491880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fdb1227c-4c76-4e71-a8b8-6c849f8210dd · outbound

This paper cites The validation scores are for our information and are not proposed to be made public.

Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features The validation scores are for our information and are not proposed to be made public

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:49.475435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 24d73cfa-c136-437f-b40e-6a66da2e2a02 · outbound

This paper cites Our proposed method is based on the use of hybrid features, which are a combination of handcrafted features and deep learning features.

Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features Our proposed method is based on the use of hybrid features, which are a combination of handcrafted features and deep learning features

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:49.457677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d422cf14-664a-4744-b59b-1274401d584d · outbound

This paper cites Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features.

Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:49.345069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.