Electrocardiogram signal classification using VGGNet: a neural network based classification model

Goswami, Agam Das; Bhavekar, Girish S.; Chafle, Pratiksha V.

doi:10.1007/s41870-022-01071-z

Cited by 20 publications

(5 citation statements)

References 46 publications

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“…Ultimately, we conducted a comprehensive comparative assessment of our proposed framework against benchmark models employed in prior research endeavors. Specifically, these benchmark models encompass ResNet-50 26 , VGGNet 27 , Hybrid CNN-LSTM 28 , and CNN-STFT 29 .…”

Section: Comparative Evaluation With Different Studiesmentioning

confidence: 99%

A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection

Wahid,

Mingliang,

Ayoub

et al. 2024

Sci Rep

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Myocardial infarction (MI) remains a significant contributor to global mortality and morbidity, necessitating accurate and timely diagnosis. Current diagnostic methods encounter challenges in capturing intricate patterns, urging the need for advanced automated approaches to enhance MI detection. In this study, we strive to advance MI detection by proposing a hybrid approach that combines the strengths of ResNet and Vision Transformer (ViT) models, leveraging global and local features for improved accuracy. We introduce a slim-model ViT design with multibranch networks and channel attention mechanisms to enhance patch embedding extraction, addressing ViT’s limitations. By training data through both ResNet and modified ViT models, we incorporate a dual-pathway feature extraction strategy. The fusion of global and local features addresses the challenge of robust feature vector creation. Our approach showcases enhanced learning capabilities through modified ViT architecture and ResNet architecture. The dual-pathway training enriches feature extraction, culminating in a comprehensive feature vector. Preliminary results demonstrate significant potential for accurate detection of MI. Our study introduces a hybrid ResNet-ViT model for advanced MI detection, highlighting the synergy between global and local feature extraction. This approach holds promise for elevating MI classification accuracy, with implications for improved patient care. Further validation and clinical applicability exploration are warranted.

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Section: Comparative Evaluation With Different Studiesmentioning

confidence: 99%

A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection

Wahid,

Mingliang,

Ayoub

et al. 2024

Sci Rep

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“…The use of deep learning techniques, particularly CNNs, has reduced the reliance on manual feature extraction in skin-lesion classification [ 34 ]. For classification tasks involving skin lesions, well-known CNN architectures like AlexNet [ 35 ], VGGNet [ 36 ], and InceptionNet [ 37 ] have been adapted and refined.…”

Section: Literature Reviewmentioning

confidence: 99%

SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network

Azeem,

Kiani,

Mansouri

et al. 2023

Cancers

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Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet’s performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet’s effectiveness, as it consistently outperformed both benchmarks across all datasets.

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“…We selected four kinds of classic convolutional networks for testing, which have won championships in the ILSVRC competition and have been successfully applied in the field of radiation source identification. These models are ConvNet [12], ResNet [24], AlexNet [35], and VGGNet [36]. Among them, ResNet adopts the same basic structure as MSCANet, and the structures of ConvNet, AlexNet, and VGGNet are shown in Figure 14.…”

Section: Conventional Deep Learning Algorithmsmentioning

confidence: 99%

“…The networks are regularized by L2 regularization with a value of 10 −4 . The ConvNet, AlexNet, and VGGNet directly adopt the structures provided in references [12,35,36], without parameter adjustment or optimization. For ResNet, the depth is reduced to 18 layers from the original architecture, and the size and quantity of convolutional kernels are optimized.…”

Section: Conventional Deep Learning Algorithmsmentioning

confidence: 99%

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

Xiong,

Pan,

2023

Remote Sensing

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Target recognition mainly focuses on three approaches: optical-image-based, echo-detection-based, and passive signal-analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an “end-to-end” model that cascades a noise estimation network with a recognition network to identify working modes in noisy environments. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signals, the recognition network is realized based on the multi-scale convolutional attention network (MSCANet). Firstly, deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, the finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem of the conventional method being seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environment, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, the proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

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Electrocardiogram signal classification using VGGNet: a neural network based classification model

Cited by 20 publications

References 46 publications

A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection

A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection

SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

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