DNN based approach to classify Covid’19 using convolutional neural network and transfer learning

Joshi, Bhavya; Sharma, Akhilesh; Yadav, Narendra Singh; Tiwari, Shamik

doi:10.1080/1206212x.2021.1983289

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…The average macro accuracy of the proposed model was compared to the other existing works; the versatility of the proposed model is indicated by the number of weld defects classified by the proposed model in the capable identification column in Table 3 . The present work is an improvement in terms of enabling the VGG16 deep neural network in terms of detecting up to 14 types of weld defects which is higher than the capability reported by Ajmi et al [ 27 ] and Liu et al [ 33 ] in their study. The present work utilized ImageNet as the benchmark for achieving 90% accuracy by using the transfer learning technique.…”

Section: Resultsmentioning

confidence: 62%

“…They demonstrated that their model could achieve excellent classification accuracy even with minimal training data, implying that deep learning approaches might be useful for weld fault recognition when data is scarce. Joshi et al [ 27 ] explored the usage of deep learning methods in identifying Covid-19-affected patients from X-ray radiographs. They analyzed datasets involving chest radiographs of affected patients to generate classification data.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based weld defect classification using VGG16 transfer learning adaptive fine-tuning

Kumaresan

Aultrin

Kumar

et al. 2023

Int J Interact Des Manuf

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Welding is a vital joining process; however, occurrences of weld defects often degrade the quality of the welded part. The risk of occurrence of a variety of defects has led to the development of advanced weld defects detection systems such as automated weld defects detection and classification. The present work is a novel approach that proposes and investigates a unique image-centered method based on a deep learning model trained by a small X-ray image dataset. A data augmentation method able to process images on the go was used to offset the limitation of the small X-ray dataset. Fine-tuned transfer learning techniques were used to train two convolutional neural network based architectures with VGG16 and ResNet50 as the base models for the augmented sets. Out of the networks we fine-tuned, VGG16 based model performed well with a relatively higher average accuracy of 90%. Even though the small dataset was spread across 15 different classes in an unbalanced way, the learning curves showed acceptable model generalization characteristics.

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Section: Resultsmentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Deep learning-based weld defect classification using VGG16 transfer learning adaptive fine-tuning

Kumaresan

Aultrin

Kumar

et al. 2023

Int J Interact Des Manuf

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“…During the epidemic, many COVID-19 image segmentation methods based on deep learning were explored. Such as (Huang et al, 2020;Zhou et al, 2020;Paluru et al, 2021) based on convolutional neural networks, (Bhattacharyya et al, 2022), based on conditional generative adversarial networks, , based on lightweight capsule networks, (Jia et al, 2023), based on graph reasoning, and (Joshi et al, 2022; combined with transfer learning. These methods have made effective contributions to the diagnosis and treatment of COVID-19 patients.…”

Section: Open Access Edited Bymentioning

confidence: 99%

ERGPNet: lesion segmentation network for COVID-19 chest X-ray images based on embedded residual convolution and global perception

Yue,

Yang,

Zhao

et al. 2023

Front. Physiol.

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The Segmentation of infected areas from COVID-19 chest X-ray (CXR) images is of great significance for the diagnosis and treatment of patients. However, accurately and effectively segmenting infected areas of CXR images is still challenging due to the inherent ambiguity of CXR images and the cross-scale variations in infected regions. To address these issues, this article proposes a ERGPNet based on embedded residuals and global perception, to segment lesion regions in COVID-19 CXR images. First, aiming at the inherent fuzziness of CXR images, an embedded residual convolution structure is proposed to enhance the ability of internal feature extraction. Second, a global information perception module is constructed to guide the network in generating long-distance information flow, alleviating the interferences of cross-scale variations on the algorithm’s discrimination ability. Finally, the network’s sensitivity to target regions is improved, and the interference of noise information is suppressed through the utilization of parallel spatial and serial channel attention modules. The interactions between each module fully establish the mapping relationship between feature representation and information decision-making and improve the accuracy of lesion segmentation. Extensive experiments on three datasets of COVID-19 CXR images, and the results demonstrate that the proposed method outperforms other state-of-the-art segmentation methods of CXR images.

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“…We will compare the efficiency of the listed methods based on word error rate characteristics of voice recognition systems that use these methods [9], specifically marking the causes that influence the characteristic the most [10].…”

Section: Introductionmentioning

confidence: 99%

Speech Models Training Technologies Comparison Using Word Error Rate

Yakubovskyi,

Morozov

2023

ACPS

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The main purpose of this work is to analyze and compare several technologies used for training speech models, including traditional approaches as Hidden Markov Models (HMMs) and more recent methods as Deep Neural Networks (DNNs). The technologies have been explained and compared using word error rate metric based on the input of 1000 words by a user with 15 decibel background noise. Word error rate metric has been ex- plained and calculated. Potential replacements for com- pared technologies have been provided, including: Atten- tion-based, Generative, Sparse and Quantum-inspired models. Pros and cons of those techniques as a potential replacement have been analyzed and listed. Data analyzing tools and methods have been explained and most common datasets used for HMM and DNN technologies have been described. Real life usage examples of both methods have been provided and systems based on them have been ana- lyzed.

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DNN based approach to classify Covid’19 using convolutional neural network and transfer learning

Cited by 6 publications

References 35 publications

Deep learning-based weld defect classification using VGG16 transfer learning adaptive fine-tuning

Deep learning-based weld defect classification using VGG16 transfer learning adaptive fine-tuning

ERGPNet: lesion segmentation network for COVID-19 chest X-ray images based on embedded residual convolution and global perception

Speech Models Training Technologies Comparison Using Word Error Rate

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