Paper Citation Record · LEDGER

Breast Tumor Classification Using EfficientNet Deep Learning Model

As of 13 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2411.17870.

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

pith.paper-citation-record.v1
2411.17870 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:50:04.111922Z

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation f3a15367-d0f5-4738-bb27-1cf0d3714712 · outbound

This paper cites Breast cancer detection and classification using deep learning xception algorithm.

Breast Tumor Classification Using EfficientNet Deep Learning Model Breast cancer detection and classification using deep learning xception algorithm

Reference 1

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Observation 6966bd65-9585-4532-8638-d62d4aac28a6 · outbound

This paper cites A fully integrated computer-aided diagnosis system for digital x-ray mammograms via deep learning detection, segmentation, and classification.

Breast Tumor Classification Using EfficientNet Deep Learning Model A fully integrated computer-aided diagnosis system for digital x-ray mammograms via deep learning detection, segmentation, and classification

Reference 2

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Observation 9d19a6e7-1343-49a8-b0e2-d37a6c789d11 · outbound

This paper cites Analyzing histological images using hybrid techniques for early detection of multi-class breast cancer based on fusion features of cnn and hand- crafted.

Breast Tumor Classification Using EfficientNet Deep Learning Model Analyzing histological images using hybrid techniques for early detection of multi-class breast cancer based on fusion features of cnn and hand- crafted

Reference 3

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Observation 56639e3f-6264-4b26-8d85-ecf8918267f7 · outbound

This paper cites Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning.

Breast Tumor Classification Using EfficientNet Deep Learning Model Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning

Reference 4

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Observation 94e4bcd5-dca0-49f5-96cb-489a97dd0115 · outbound

This paper cites Going deeper: magnification-invariant approach for breast can- cer classification using histopathological images.

Breast Tumor Classification Using EfficientNet Deep Learning Model Going deeper: magnification-invariant approach for breast can- cer classification using histopathological images

Reference 5

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Observation 727a580e-42fb-4419-8c25-d6650a78a12f · outbound

This paper cites Fabnet: A features agglomeration-based convolutional neural network for multiscale breast cancer histopathology images classi- fication.

Breast Tumor Classification Using EfficientNet Deep Learning Model Fabnet: A features agglomeration-based convolutional neural network for multiscale breast cancer histopathology images classi- fication

Reference 6

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Observation 2749c0da-9ee9-4f25-8f65-bc325d6e3de9 · outbound

This paper cites An svm approach towards breast cancer classifica- tion from h&e-stained histopathology images based on integrated features.

Breast Tumor Classification Using EfficientNet Deep Learning Model An svm approach towards breast cancer classifica- tion from h&e-stained histopathology images based on integrated features

Reference 7

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Observation 0f906654-9e55-4012-813a-493b64a4e0ed · outbound

This paper cites Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images.

Breast Tumor Classification Using EfficientNet Deep Learning Model Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

Reference 8

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Observation c072f6fb-1aa0-4814-a7a9-2c77b42cce25 · outbound

This paper cites Drda- net: Dense residual dual-shuffle attention network for breast cancer classification using histopathological images.

Breast Tumor Classification Using EfficientNet Deep Learning Model Drda- net: Dense residual dual-shuffle attention network for breast cancer classification using histopathological images

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:50:04.533276Z

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Observation 9ea403b4-02b3-41a2-b9b0-d464ffec7ec0 · outbound

This paper cites Pcct: Progressive class-center triplet loss for imbalanced medical image classification.

Breast Tumor Classification Using EfficientNet Deep Learning Model Pcct: Progressive class-center triplet loss for imbalanced medical image classification

Reference 10

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Observation 477fb3bb-01b4-47c7-9864-22c0156297d9 · outbound

This paper cites Personalized retrogress-resilient federated learning toward imbalanced medical data.

Breast Tumor Classification Using EfficientNet Deep Learning Model Personalized retrogress-resilient federated learning toward imbalanced medical data

Reference 11

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Observation 2a997a17-b32e-491b-a258-dc2ba599e715 · outbound

This paper cites Metacost: A general method for making classifiers cost-sensitive.

Breast Tumor Classification Using EfficientNet Deep Learning Model Metacost: A general method for making classifiers cost-sensitive

Reference 12

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Observation bac4eee0-77e4-418b-8e7f-5ecb278a67c8 · outbound

This paper cites Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network.

Breast Tumor Classification Using EfficientNet Deep Learning Model Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network

Reference 13

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Observation cab54083-0d24-4f87-a234-421731b205d1 · outbound

This paper cites Breast cancer multi-classification from histopathological images with structured deep learning model.

Breast Tumor Classification Using EfficientNet Deep Learning Model Breast cancer multi-classification from histopathological images with structured deep learning model

Reference 14

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Observation 72fa3a70-15a5-4c07-a23c-6889635ca56d · outbound

This paper cites Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review.

Breast Tumor Classification Using EfficientNet Deep Learning Model Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

Reference 15

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Observation cac50994-7531-4ae0-b043-32b4cc9099a2 · outbound

This paper cites Modality specific cbam-vggnet model for the classification of breast histopathology images via transfer learning.

Breast Tumor Classification Using EfficientNet Deep Learning Model Modality specific cbam-vggnet model for the classification of breast histopathology images via transfer learning

Reference 16

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Observation af0156d4-b9c3-4dc2-a4f0-91960d329e44 · outbound

This paper cites Revolutionizing breast cancer diagnosis: A concate- nated precision through transfer learning in histopathological data analysis.Diagnostics, 14(4):422, 2024.

Breast Tumor Classification Using EfficientNet Deep Learning Model Revolutionizing breast cancer diagnosis: A concate- nated precision through transfer learning in histopathological data analysis.Diagnostics, 14(4):422, 2024

Reference 17

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Observation b7663ce3-06f7-47da-8fd3-9de103bd8e56 · outbound

This paper cites Glnet: global–local cnn’s-based informed model for detection of breast cancer categories from histopathological slides.

Breast Tumor Classification Using EfficientNet Deep Learning Model Glnet: global–local cnn’s-based informed model for detection of breast cancer categories from histopathological slides

Reference 18

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Observation 9169845b-bf2e-4337-aced-3caf18097e22 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Breast Tumor Classification Using EfficientNet Deep Learning Model Imagenet classification with deep convolutional neural networks

Reference 19

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Observation b748c012-aafe-48f5-a9d3-9aa5efc5d6be · outbound

This paper cites A clinical decision support tool to detect invasive ductal carcinoma in histopathological images using support vector machines, na ¨ ıve-bayes, and k-nearest neighbor classifiers.

Breast Tumor Classification Using EfficientNet Deep Learning Model A clinical decision support tool to detect invasive ductal carcinoma in histopathological images using support vector machines, na ¨ ıve-bayes, and k-nearest neighbor classifiers

Reference 20

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Observation 1e7e076c-e90d-46e8-b7ba-f10763826607 · outbound

This paper cites Breast cancer diagnosis from histopathology images using deep neural network and xgboost.

Breast Tumor Classification Using EfficientNet Deep Learning Model Breast cancer diagnosis from histopathology images using deep neural network and xgboost

Reference 21

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Observation 2104187c-8e5d-48c7-988e-041a18d13ff4 · outbound

This paper cites Classification of breast cancer histopathological images using discriminative patches screened by generative adversarial networks.

Breast Tumor Classification Using EfficientNet Deep Learning Model Classification of breast cancer histopathological images using discriminative patches screened by generative adversarial networks

Reference 22

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Observation e77bebe3-5a61-410c-b0f1-78b0c35de7f0 · outbound

This paper cites Cancer treatment and survivorship statistics, 2022.

Breast Tumor Classification Using EfficientNet Deep Learning Model Cancer treatment and survivorship statistics, 2022

Reference 23

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Observation b9de3d4b-37d3-4528-a761-e158d96d75fd · outbound

This paper cites Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges.

Breast Tumor Classification Using EfficientNet Deep Learning Model Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges

Reference 24

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Observation f08f93f6-88ea-465c-8844-c248154193e0 · outbound

This paper cites Multi-class breast cancer classification using deep learning convolutional neural network.

Breast Tumor Classification Using EfficientNet Deep Learning Model Multi-class breast cancer classification using deep learning convolutional neural network

Reference 25

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Observation 18baf6aa-25d9-4791-9af7-8f828610ad0c · outbound

This paper cites Classification of breast cancer histology images using alexnet.

Breast Tumor Classification Using EfficientNet Deep Learning Model Classification of breast cancer histology images using alexnet

Reference 26

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Observation a9b40fa7-476a-4819-8168-29cf80f1ac06 · outbound

This paper cites Who position paper on mammography screening.

Breast Tumor Classification Using EfficientNet Deep Learning Model Who position paper on mammography screening

Reference 27

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Observation a8e059df-4f7c-4ef6-bc93-dd6689af86c5 · outbound

This paper cites Machine learning and deep learning approach for medical image analysis: diagnosis to detection.

Breast Tumor Classification Using EfficientNet Deep Learning Model Machine learning and deep learning approach for medical image analysis: diagnosis to detection

Reference 28

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Observation 66abd9eb-943b-41c1-b414-03ad793d948f · outbound

This paper cites Detection of breast cancer using histopathological image classification dataset with deep learning techniques.

Breast Tumor Classification Using EfficientNet Deep Learning Model Detection of breast cancer using histopathological image classification dataset with deep learning techniques

Reference 29

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Observation 61db2744-8f2f-4396-80f9-06f16b017be0 · outbound

This paper cites Histopathological classi- fication of breast cancer images using a multi-scale input and multi-feature network.

Breast Tumor Classification Using EfficientNet Deep Learning Model Histopathological classi- fication of breast cancer images using a multi-scale input and multi-feature network

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:50:04.232840Z

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Observation a968bb1d-8aa1-46c2-9eec-66bf369792cc · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Breast Tumor Classification Using EfficientNet Deep Learning Model Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 31

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Observation 95eb5700-785c-4bad-aa05-fcfa2aecdbe6 · outbound

This paper cites A dataset for breast cancer histopathological image classification.

Breast Tumor Classification Using EfficientNet Deep Learning Model A dataset for breast cancer histopathological image classification

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:50:04.218015Z

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Observation ae82a02b-6ccf-4624-9170-ef1e605b90c6 · outbound

This paper cites Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid cnn-lstm based transfer learning.

Breast Tumor Classification Using EfficientNet Deep Learning Model Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid cnn-lstm based transfer learning

Reference 33

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Observation c90ed930-a8ad-4708-b6fb-b70fb56ad189 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Breast Tumor Classification Using EfficientNet Deep Learning Model Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 34

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Observation d3026b24-8dcf-412a-97f5-0e1f27b52044 · outbound

This paper cites Secs: An effective cnn joint construction strategy for breast cancer histopathological image classification.

Breast Tumor Classification Using EfficientNet Deep Learning Model Secs: An effective cnn joint construction strategy for breast cancer histopathological image classification

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:50:04.179637Z

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Observation e29d00c0-035d-4ba7-9381-27e01e2c73d2 · outbound

This paper cites De-enhancing the dynamic contrast-enhanced breast mri for robust registration.

Breast Tumor Classification Using EfficientNet Deep Learning Model De-enhancing the dynamic contrast-enhanced breast mri for robust registration

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:50:04.164669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

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