Paper Citation Record · LEDGER

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off

As of 8 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2505.21597.

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

pith.paper-citation-record.v1
2505.21597 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:34:11.442534Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

31 of 31 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7b713876-7f92-4164-adc1-11eb6ab3187e · outbound

This paper cites an unresolved cited work.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Unresolved cited work

Reference 1

Resolution: verified exact; doi, observed 2026-08-07T13:34:12.037177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bca10fa1-0219-42ef-bd52-a23081fd563e · outbound

This paper cites Revolutionizing image recognition: Next-generation cnn architectures for handwritten digits and objects,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Revolutionizing image recognition: Next-generation cnn architectures for handwritten digits and objects,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.998569Z

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Observation f958dfd4-c326-4407-a573-2b4dd5c28014 · outbound

This paper cites Healthcare professionals creden- tial verification model using blockchain-based self-sovereign identity,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Healthcare professionals creden- tial verification model using blockchain-based self-sovereign identity,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.814815Z

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Observation e67cb4f4-d3d8-4839-a2fa-c0a66e352d43 · outbound

This paper cites Highly sensitive terahertz metasurface based on electromagnetically induced transparency-like resonance in detection of skin cancer cells,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Highly sensitive terahertz metasurface based on electromagnetically induced transparency-like resonance in detection of skin cancer cells,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.693531Z

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Observation 0221697b-72ea-4ef2-b503-04e658b45557 · outbound

This paper cites Melanoma management: From epidemiology to treatment and latest advances,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Melanoma management: From epidemiology to treatment and latest advances,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.540304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b58aff42-0653-4b21-bc27-2750bd4cb78e · outbound

This paper cites Anomaly detection in biomedical data and image using various shallow and deep learning algorithms,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Anomaly detection in biomedical data and image using various shallow and deep learning algorithms,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.413001Z

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Observation 36d16412-7fe8-4731-8a39-6b21eea32ef4 · outbound

This paper cites an unresolved cited work.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Unresolved cited work

Reference 7

Resolution: verified exact; doi, observed 2026-08-07T13:34:11.920903Z

Source-reported events for the cited work

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Observation 0b000c96-307f-45ff-a53e-c0d61cb4f44d · outbound

This paper cites Gan-based data augmentation and anonymization for skin-lesion analysis: A critical review,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Gan-based data augmentation and anonymization for skin-lesion analysis: A critical review,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.228045Z

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Observation 9f17ab52-a670-4bda-a2b3-710fe874836f · outbound

This paper cites Deep learning-based system for automatic melanoma detection,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Deep learning-based system for automatic melanoma detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.053958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4775cd6e-8258-4ced-a333-fb5f0b4295e0 · outbound

This paper cites The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:15.862178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 334a1d31-8260-41b6-96e3-c85f5b7313db · outbound

This paper cites Innovative way of identifying skin cancer model design with fcnn and lstm,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Innovative way of identifying skin cancer model design with fcnn and lstm,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:15.648038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c9d52327-d4f9-408b-88ba-832dd153137c · outbound

This paper cites Skin lesion segmentation based on multi-scale attention convolutional neural network,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Skin lesion segmentation based on multi-scale attention convolutional neural network,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:15.422060Z

Source-reported events for the cited work

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Observation af90829c-99f5-4862-9b23-be081b696065 · outbound

This paper cites Transfer learning and fine-tuned transfer learning methods’ effectiveness analyse in the cnn-based deep learning models,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Transfer learning and fine-tuned transfer learning methods’ effectiveness analyse in the cnn-based deep learning models,

Reference 13

Resolution: verified exact; doi, observed 2026-08-07T13:34:11.804295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6dd4d1cb-fef5-42bf-bce4-48d916ae9b73 · outbound

This paper cites Run, don’t walk: Chasing higher flops for faster neural networks,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Run, don’t walk: Chasing higher flops for faster neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:15.234686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50bd79d0-95fb-449d-9fa3-326fd9985c4c · outbound

This paper cites FALCON: FLOP-aware combinatorial optimization for neural network pruning,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off FALCON: FLOP-aware combinatorial optimization for neural network pruning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:15.089578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 03fe8b61-6e98-4206-81df-e041598a875f · outbound

This paper cites Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:34:12.174470Z

Source-reported events for the cited work

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Observation 37875f37-56da-4596-bbc4-629c78a0de82 · outbound

This paper cites A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off A comparative analysis of deep learning and hybrid models to diagnose multi-class skin cancer,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:14.821050Z

Source-reported events for the cited work

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Observation cbcfea9c-5b9a-4220-bbf5-240e958df942 · outbound

This paper cites Melanoma segmentation: A framework of improved densenet77 and unet convolutional neural network,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Melanoma segmentation: A framework of improved densenet77 and unet convolutional neural network,

Reference 18

Resolution: verified exact; doi, observed 2026-08-07T13:34:11.667913Z

Source-reported events for the cited work

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Observation d718ca2a-4428-4a63-beaa-470401e3bf7f · outbound

This paper cites Using imagenet xception model to identify skin cancer and non-skin cancer image classification,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Using imagenet xception model to identify skin cancer and non-skin cancer image classification,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:14.555167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 60b3781e-64b9-44ff-9c83-d01f3e0aba8c · outbound

This paper cites Skin cancer classifi- cation and detection using vgg-19 and desnet,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Skin cancer classifi- cation and detection using vgg-19 and desnet,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:14.257474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 57858a95-fe8c-45e1-b224-6d982f960877 · outbound

This paper cites Benign and malignant skin lesion detection from melanoma skin cancer images,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Benign and malignant skin lesion detection from melanoma skin cancer images,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:13.981918Z

Source-reported events for the cited work

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Observation 87d1ae81-4fbb-475a-b9b5-bc297e299633 · outbound

This paper cites Diagnosis of skin cancer via transfer learning with combined channel attention and spatial atten- tion,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Diagnosis of skin cancer via transfer learning with combined channel attention and spatial atten- tion,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:13.756064Z

Source-reported events for the cited work

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Observation 5c68d86f-3f06-4db4-bf55-0423bb70c218 · outbound

This paper cites Deepskin: A deep learning approach for skin cancer classification,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Deepskin: A deep learning approach for skin cancer classification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:13.488469Z

Source-reported events for the cited work

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Observation b9498ff9-70b3-490a-acf6-9c811125866c · outbound

This paper cites Dermatologist-level classification of skin cancer using cascaded en- sembling of convolutional neural network and handcrafted features based deep neural network,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Dermatologist-level classification of skin cancer using cascaded en- sembling of convolutional neural network and handcrafted features based deep neural network,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:13.234875Z

Source-reported events for the cited work

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Observation 1dc82b78-65a9-4f62-8fc2-34757ffa80d4 · outbound

This paper cites Enhanced magneto-optic imaging based on lstm-cnn model and multi-modal fusion,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Enhanced magneto-optic imaging based on lstm-cnn model and multi-modal fusion,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:13.035393Z

Source-reported events for the cited work

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Observation 4e99f3cc-f396-4161-9afa-4e40222bdf96 · outbound

This paper cites Cu-net: A new improved multi-input color u- net model for skin lesion semantic segmentation,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Cu-net: A new improved multi-input color u- net model for skin lesion semantic segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:12.869039Z

Source-reported events for the cited work

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Observation 8b4e4a5d-ac75-47fd-935c-9830615a4c4a · outbound

This paper cites Analysis and prediction of energy consumption in neural networks based on machine learning,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Analysis and prediction of energy consumption in neural networks based on machine learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:12.734033Z

Source-reported events for the cited work

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Observation c076efe5-a5ef-437f-aaca-43a6bf5e6f69 · outbound

This paper cites Mcmc: Multi- constrained model compression via one-stage envelope reinforcement learning,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Mcmc: Multi- constrained model compression via one-stage envelope reinforcement learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:12.577071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c04df405-d152-47d1-bf57-7742d83cbd65 · outbound

This paper cites The HAM10000 dataset, a large collection of multi- source dermatoscopic images of common pigmented skin lesions,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off The HAM10000 dataset, a large collection of multi- source dermatoscopic images of common pigmented skin lesions,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:12.429622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3ebf10c7-f05e-4132-abbb-1b9ffbbae4c2 · outbound

This paper cites Order depen- dency in sequential correlation,.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Order depen- dency in sequential correlation,

Reference 30

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Observation 947927cd-4c0a-4cdb-957c-b20af287b586 · outbound

This paper cites Available: https://doi.org/10.7910/DVN/DBW86T.

Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off Available: https://doi.org/10.7910/DVN/DBW86T

Reference 2018

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.