Research of image recognition method based on enhanced inception-ResNet-V2

Peng, Cheng; Liu, Yikun; Yuan, Xinpan; Chen, Qing

doi:10.1007/s11042-022-12387-0

Cited by 28 publications

(11 citation statements)

References 4 publications

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“…In particular, the remaining variants experience instability, and the network cannot be trained when the number of filters exceeds 1000. Therefore, the residual connection scaling contributes to stabilizing the network training [ 50 ]. Figure 8 shows the compressed structure of the InceptionResNetV2 network.…”

Section: Methodsmentioning

confidence: 99%

Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis

Dzierżak

Omiotek

2022

Sensors

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The aim of this study was to assess the possibility of using deep convolutional neural networks (DCNNs) to develop an effective method for diagnosing osteoporosis based on CT images of the spine. The research material included the CT images of L1 spongy tissue belonging to 100 patients (50 healthy and 50 diagnosed with osteoporosis). Six pre-trained DCNN architectures with different topological depths (VGG16, VGG19, MobileNetV2, Xception, ResNet50, and InceptionResNetV2) were used in the study. The best results were obtained for the VGG16 model characterised by the lowest topological depth (ACC = 95%, TPR = 96%, and TNR = 94%). A specific challenge during the study was the relatively small (for deep learning) number of observations (400 images). This problem was solved using DCNN models pre-trained on a large dataset and a data augmentation technique. The obtained results allow us to conclude that the transfer learning technique yields satisfactory results during the construction of deep models for the diagnosis of osteoporosis based on small datasets of CT images of the spine.

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Section: Methodsmentioning

confidence: 99%

Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis

Dzierżak

Omiotek

2022

Sensors

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“…In this paper [5], a supervised machine based on extreme learning machines is suggested for efficient spammer identification. The study initially creates the labelled dataset by crawling Sina Weibo data and categorising related users into non-spammer and spammer groups.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Approach for Spam Comment Detection on YouTube Using Graph-Enhanced Hierarchical Attention Network (GE-HAN)

Ganguly,

Ruby,

Reddy

et al. 2024

Preprint

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Spam comments pose a significant challenge in maintaining the quality of online platforms, including YouTube. In this paper, we propose a novel approach, the Graph-Enhanced Hierarchical Attention Network (GE-HAN), for spam comment detection. Our approach leverages the content of comments, the hierarchical structure of conversations, and user interactions to accurately classify comments as spam or non-spam. The GE-HAN model combines attention mechanisms to capture important textual features within comments and graph convolutional networks to analyze user interactions and detect coordinated spamming efforts. We trained and evaluated the model using a labeled dataset of spam comments, incorporating user names along with comment content and adjacency matrix representing user interactions. Experimental results demonstrate that the GE-HAN model achieves superior performance in spam detection, outperforming traditional methods. By considering both content and user dynamics, the model effectively identifies spam comments in YouTube, providing a robust solution to combat spam. Our research contributes to the field by showcasing the potential of graph-enhanced attention mechanisms in capturing complex patterns within comments and user interactions.

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“…Increasing the network depth prevents the problems of gradient disappearance and gradient explosion and improves the network performance. The structure of the network model is shown in Figure 6 [33].…”

Section: Algorithm Designmentioning

confidence: 99%

Intelligent identification technology for high‐order digital modulation signals under low signal‐to‐noise ratio conditions

Zha

Wang

Shen

et al. 2023

IET Signal Processing

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Research of image recognition method based on enhanced inception-ResNet-V2

Cited by 28 publications

References 4 publications

Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis

Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis

A Novel Approach for Spam Comment Detection on YouTube Using Graph-Enhanced Hierarchical Attention Network (GE-HAN)

Intelligent identification technology for high‐order digital modulation signals under low signal‐to‐noise ratio conditions

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