Few-Shot Learning Approach for COVID-19 Detection from X-Ray Images

Abdrakhmanov, Rakhat; Altynbekov, Meirzhan; Abu, Assanali; Shomanov, Adai; Viderman, Dmitriy

doi:10.1109/icecco53203.2021.9663860

Cited by 10 publications

(5 citation statements)

References 6 publications

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“… [28] EDL-COVID COVIDx 95 96 – T. Ozturk [56] DarkCovidNet 1127 CXR images 87.02 85.35 92.18 Ioannis D. [57] Transfer Learning 1427 CXR images 96.78 98.66 96.46 E. Luz [58] EfficientNet Family 13770 CXR images 93.9 96.8 – E. Hussain [59] CoroDet 7390 CXR images 94.2 94.2 96.2 A. I. Khan [60] CoroNet 921 CXR images 95 – 97.5 Chuchan et al. [61] Transfer Learning 5232 CXR images 96.39 99.62 – Brunese [62] Transfer Learning with VGG-16 6523 CXR images 97 96 98 R. Abdrakhmanov [63] Few-Shot Learning Approach 6207 CXR images 97.7 – – …”

Section: Resultsmentioning

confidence: 99%

Ensemble of Deep Neural Networks based on Condorcet’s Jury Theorem for screening Covid-19 and Pneumonia from radiograph images

Srivastava

Pradhan

Saini

2022

Computers in Biology and Medicine

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Section: Resultsmentioning

confidence: 99%

Ensemble of Deep Neural Networks based on Condorcet’s Jury Theorem for screening Covid-19 and Pneumonia from radiograph images

Srivastava

Pradhan

Saini

2022

Computers in Biology and Medicine

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“…Abdrakhmanov et al [33] proposed a few-shot learning approach based on a convolutional neural network for COVID-19 diagnosis using chest Xray images. The authors demonstrated that their method achieved high accuracy even with a small number of labeled samples.…”

Section: Related Workmentioning

confidence: 99%

A Few-Shot Learning Approach for Covid-19 Diagnosis Using Quasi-Configured Topological Spaces

Liu,

Wang,

Jiang

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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Accurate and efficient COVID-19 diagnosis is crucial in clinical settings. However, the limited availability of labeled data poses a challenge for traditional machine learning algorithms. To address this issue, we propose Turning Point (TP), a few-shot learning (FSL) approach that leverages high-level turning point mappings to build sophisticated representations across previously labeled data. Unlike existing FSL models, TP learns using quasi-configured topological spaces and efficiently combines the outputs of diverse TP learners. We evaluated TPFSL using three COVID-19 datasets and compared it with seven different benchmarks. Results show that TPFSL outperformed the top-performing benchmark models in both one-shot and five-shot tasks, with an average improvement of 4.50% and 4.43%, respectively. Additionally, TPFSL significantly outperformed the ProtoNet benchmark by 12.966% and 11.033% in one-shot and five-shot classification problems across all datasets. Ablation experiments were also conducted to analyze the impact of variables such as TP density, network topology, distance measure, and TP placement. Overall, TPFSL has the potential to improve the accuracy and speed of diagnoses for COVID-19 in clinical settings and can be a valuable tool for medical professionals.

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“…Abdrakhmanov et al [24] employed few-shot learning techniques to classify images with a small amount of training data and achieved a classification accuracy of 97.7%.…”

Section: A Datasetmentioning

confidence: 99%

“…The DenseNet + SVM method achieved a classification accuracy of 96.29%.Sohan et al[21] employed ResNet-18 and VGG16 transfer learning models on the same dataset, achieving an accuracy of 97% in their study.Sahlol et al[22] proposed a hybrid classification approach that utilized the Marine Predators algorithm to select a swarmbased feature from the InceptionV3 transfer learning model. In their study, they achieved an accuracy of 98.7% and an F-score of 98.2%.Abdollahi et al[23] achieved an accuracy of 97.99% in their study using the VGG16 transfer learning model.Abdrakhmanov et al[24] employed few-shot learning techniques to classify images with a small amount of training data and achieved a classification accuracy of 97.7%.…”

mentioning

confidence: 99%

Designing Effective Models for COVID-19 Diagnosis through Transfer Learning and Interlayer Visualization

ÖZDEMİR

2023

Balkan Journal of Electrical and Computer Engineering

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Creating a model from scratch that fits the dataset can be laborious and time-consuming. The level of difficulty in designing a new model can vary depending on factors such as the complexity of the model and the size and characteristics of the dataset. Factors such as the number of variables in the dataset, the structure of the data, class imbalance, and the size of the dataset are important in deciding which model to use. In addition, long experimental studies are required to design the most appropriate model for the dataset. In this study, we investigated how transfer learning models can be utilized to solve this problem. Experimental studies were conducted on the Covid-19 dataset with transfer learning models and the most successful transfer learning models were identified. Then, layers that did not contribute to the performance of the transfer learning models and could not extract the necessary features from the dataset were identified and removed from the model. After removing the unnecessary layers from the model, new models with fast, less complex and fewer parameters were obtained. In the studies conducted with the new models derived from the most successful transfer learning models with the inter-layer imaging method, the classes were classified with an accuracy of %98.8 and the images belonging to the Covid-19 class were classified with a precision of %99.7.

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Few-Shot Learning Approach for COVID-19 Detection from X-Ray Images

Cited by 10 publications

References 6 publications

Ensemble of Deep Neural Networks based on Condorcet’s Jury Theorem for screening Covid-19 and Pneumonia from radiograph images

Ensemble of Deep Neural Networks based on Condorcet’s Jury Theorem for screening Covid-19 and Pneumonia from radiograph images

A Few-Shot Learning Approach for Covid-19 Diagnosis Using Quasi-Configured Topological Spaces

Designing Effective Models for COVID-19 Diagnosis through Transfer Learning and Interlayer Visualization

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