Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-23T06:26:06.948773Z
Paper Citation Record · LEDGER
The Potential of Convolutional Neural Networks for Cancer Detection
As of 17 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2412.17155.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
21 of 21 outbound references displayed
- verified exact1
- verified fuzzy9
- unresolved11
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 429687dd-1fb6-4684-baac-0bd3bc7c9455 · outbound
This paper cites These changes (gradients) indicate how much the weights and biases should change in order to reduce the cost function.
The Potential of Convolutional Neural Networks for Cancer Detection These changes (gradients) indicate how much the weights and biases should change in order to reduce the cost function
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:27:38.719279Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7bbab43a-66a8-428b-bac8-48246ebd0e0b · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.728122Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3c7bfe5e-f07c-4ac0-b31c-073034b0627e · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.712989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7c620b8d-9f7f-4a06-a9ea-6191ab43361e · outbound
This paper cites Then, the proposed model achieved 95.98% on the test data, which outperformed two pre-trained models, including GoogLeNet and MobileNet.
The Potential of Convolutional Neural Networks for Cancer Detection Then, the proposed model achieved 95.98% on the test data, which outperformed two pre-trained models, including GoogLeNet and MobileNet
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:27:38.744656Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 57a34397-61e9-4a1e-92d2-a81ffd26d82c · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.759168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4bce03e3-ad2b-453f-af6d-a74f93c22fea · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.706533Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c9f3637b-43f3-4256-8eaf-f4fb2f09f318 · outbound
This paper cites Abdominal CT images often have low contrast and blurriness, making liver segmentation challenging.
The Potential of Convolutional Neural Networks for Cancer Detection Abdominal CT images often have low contrast and blurriness, making liver segmentation challenging
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:27:38.710096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4a7a0a94-bc5f-44b7-810d-8a958387e3e3 · outbound
This paper cites The background was discarded by eliminating zero-intensity pixels, and the image was refined using Otsu’s grayscale thresholding method.
The Potential of Convolutional Neural Networks for Cancer Detection The background was discarded by eliminating zero-intensity pixels, and the image was refined using Otsu’s grayscale thresholding method
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:27:38.738480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ba1a3fb9-358d-4eef-a0a2-28f78ccb6a63 · outbound
This paper cites These combined techniques improved the quality and clarity of breast cancer images.59 The DDSM images were randomly divided into 70% for training and 30% for testing.
The Potential of Convolutional Neural Networks for Cancer Detection These combined techniques improved the quality and clarity of breast cancer images.59 The DDSM images were randomly divided into 70% for training and 30% for testing
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:27:38.703644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 29c1be73-770c-4a90-b87d-331c16365532 · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.722167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 94eecf86-f5b6-4073-bd98-008157667a24 · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 11
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.700085Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 716350f4-e245-42ff-ae1a-475308cb6e38 · outbound
This paper cites These methods can automatically identify significant features without requiring manual feature extraction.
The Potential of Convolutional Neural Networks for Cancer Detection These methods can automatically identify significant features without requiring manual feature extraction
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:27:38.725107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2f3d94d6-502e-438f-b360-da9854db4b52 · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.715906Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 950f2f18-406c-4678-bcc5-002869ce6799 · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.747409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation dbc73227-955b-4413-a253-0fad38301a6b · outbound
This paper cites The ConvLSTM model demonstrated the best performance with a Peak AUC of 0.98, outperforming the 3D CNN model (Peak AUC = 0.92) and the VGG model (Peak AUC = 0.86).
The Potential of Convolutional Neural Networks for Cancer Detection The ConvLSTM model demonstrated the best performance with a Peak AUC of 0.98, outperforming the 3D CNN model (Peak AUC = 0.92) and the VGG model (Peak AUC = 0.86)
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:27:38.734548Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bd8f8f49-9bc3-41f9-9918-377480d95f1d · outbound
This paper cites This stage evaluates the impact of the type of medical images (CT and ultrasound) on diagnostic results, with model tuning applied to different datasets.
The Potential of Convolutional Neural Networks for Cancer Detection This stage evaluates the impact of the type of medical images (CT and ultrasound) on diagnostic results, with model tuning applied to different datasets
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:27:38.731246Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 131d7770-fc04-4882-8c18-f97bb43a37d0 · outbound
This paper cites Three optional architectures were proposed: • SIDC (Single Input Dual Channel): Combines input channels into a unified model.
The Potential of Convolutional Neural Networks for Cancer Detection Three optional architectures were proposed: • SIDC (Single Input Dual Channel): Combines input channels into a unified model
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T06:27:38.696814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4a10fd74-40db-41e3-acd3-53a460d494d0 · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.750263Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c1393735-87d6-4927-b293-1deaf5f3fae7 · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.755927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7365b0db-a2d7-4a1a-a27b-266429db9670 · outbound
This paper cites an unresolved cited work.
The Potential of Convolutional Neural Networks for Cancer Detection Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T06:27:38.741544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7c6d9286-651b-4942-91da-f56a05f852bd · outbound
This paper cites normal" patients (accuracy = 1.00) while the DIDC performs better with respect to.
The Potential of Convolutional Neural Networks for Cancer Detection normal" patients (accuracy = 1.00) while the DIDC performs better with respect to
Reference 21
- Resolution
- verified exact
- doi, observed 2026-05-23T06:27:38.139161Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
No inbound Pith citation observations are available.