Paper Citation Record · LEDGER

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining

As of 18 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2509.08586.

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

pith.paper-citation-record.v1
2509.08586 v1

Coverage vector

measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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External citation measurements

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Outbound references

Observation acaa0181-bc90-4936-bba4-6a13e6f2bb86 · outbound

This paper cites Pneumonia,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Pneumonia,

Reference 1

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Observation 708453c2-26e3-4f29-b8d4-790322dc1e0b · outbound

This paper cites Go- ing deeper with Image Transformers ,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Go- ing deeper with Image Transformers ,

Reference 2

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Observation 70e09be5-f556-4b83-b4a1-d508acfb1fe7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 3

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This paper cites Comparing vision trans- formers and convolutional neural networks for image classification: A literature review,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Comparing vision trans- formers and convolutional neural networks for image classification: A literature review,

Reference 4

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Observation a4dce718-a7d3-4d93-ab68-60d67b9ac406 · outbound

This paper cites Prediction of the gain in classification performance from combining multiple imaging modalities,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Prediction of the gain in classification performance from combining multiple imaging modalities,

Reference 5

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This paper cites Medical data visualiza- tion analysis and processing based on machine learning,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Medical data visualiza- tion analysis and processing based on machine learning,

Reference 6

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Observation cc7a65ec-4732-44d2-accf-4c0559c1ba6f · outbound

This paper cites Image processing using pearsons correlation coefficient: Applications on autonomous robotics,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Image processing using pearsons correlation coefficient: Applications on autonomous robotics,

Reference 7

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Observation 1d321179-3e30-474c-ab04-7c4148cf0912 · outbound

This paper cites The relationship between depth of vocabulary knowledge and reading comprehension of iranian efl learners,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining The relationship between depth of vocabulary knowledge and reading comprehension of iranian efl learners,

Reference 8

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This paper cites Mutual-information-based reg- istration of medical images: a survey,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Mutual-information-based reg- istration of medical images: a survey,

Reference 9

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This paper cites Support-vector networks,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Support-vector networks,

Reference 10

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Observation f266f38b-e034-43c8-af11-abfc21908453 · outbound

This paper cites Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison,

Reference 11

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Observation 555db1cb-288f-4c7e-acff-e42a3709e3ed · outbound

This paper cites Support vector machine for content-based image retrieval: A comprehensive overview,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Support vector machine for content-based image retrieval: A comprehensive overview,

Reference 12

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This paper cites Libsvm: A library for support vector machines,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Libsvm: A library for support vector machines,

Reference 13

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This paper cites Comparison of medical image classi- fication accuracy among three machine learning methods,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Comparison of medical image classi- fication accuracy among three machine learning methods,

Reference 14

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Observation 81bad04e-4d0d-4aa1-83a3-d2e2696b6d9d · outbound

This paper cites Visualizing and understanding convolu- tional networks,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Visualizing and understanding convolu- tional networks,

Reference 15

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This paper cites Image copy-move forgery detection via an end-to-end deep neural network,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Image copy-move forgery detection via an end-to-end deep neural network,

Reference 16

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This paper cites Imagenet classification with deep convolutional neural networks,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Imagenet classification with deep convolutional neural networks,

Reference 17

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Observation 9f081b50-fcd8-4ae7-adbc-bd3fb0a7d03f · outbound

This paper cites Do vision transformers see like convolutional neural networks?.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Do vision transformers see like convolutional neural networks?

Reference 18

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Observation 633757c2-3f41-449e-b7c7-9bb71ae7cf9e · outbound

This paper cites Lightweight parallel cnn to classify covid- 19 associated pneumonia from chest x-ray,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Lightweight parallel cnn to classify covid- 19 associated pneumonia from chest x-ray,

Reference 19

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This paper cites Are pre-trained convolutions better than pre-trained trans- formers?.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Are pre-trained convolutions better than pre-trained trans- formers?

Reference 20

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Observation bd60a5a9-080f-49f3-bc3b-53577535a4a8 · outbound

This paper cites A two-step hybrid cnn-vit model for chest disease classification,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining A two-step hybrid cnn-vit model for chest disease classification,

Reference 21

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This paper cites Exploring the synergies of hybrid cnns and vits architectures for computer vision,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Exploring the synergies of hybrid cnns and vits architectures for computer vision,

Reference 22

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Observation 60fcc7b1-90ed-4372-86ea-15beb847810f · outbound

This paper cites Explainable hybrid transformer for multi-classification of lung disease using chest x-rays,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Explainable hybrid transformer for multi-classification of lung disease using chest x-rays,

Reference 23

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This paper cites Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer,

Reference 24

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 25

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This paper cites Chest x-ray images (pneumonia),.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Chest x-ray images (pneumonia),

Reference 26

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This paper cites The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression,

Reference 27

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Observation 56a03c72-0c49-47c2-b3cf-59ad9aa01941 · outbound

This paper cites Handling imbalanced data: A survey,.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Handling imbalanced data: A survey,

Reference 28

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Impact of training set batch size on the performance of convolutional neural networks for diverse datasets,

Reference 29

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Stratified sampling,

Reference 30

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Deep Residual Learning for Image Recognition

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Vision transformer (vit)-based applications in image classification,

Reference 32

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Deep convolutional neural networks for image classification: A comprehensive review,

Reference 33

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining A review of convolutional neural networks in computer vision,

Reference 34

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Goodfellow, Y

Reference 35

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining An Introduction to Convolutional Neural Networks

Reference 36

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Attention is all you need,

Reference 37

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining An introduction to transformers,

Reference 38

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Global weighted average pooling bridges pixel-level localization and image-level classification,

Reference 39

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks

Reference 40

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This paper cites Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification.

CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification

Reference 41

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CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining Power laws, pareto distributions and zipf’s law,

Reference 42

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Pith citing papers

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