RETRACTED ARTICLE: Burn Image Recognition of Medical Images Based on Deep Learning: From CNNs to Advanced Networks

Wu, Xiaolin; He-ming, Chen; Wu, Xiaoli; Wu, Shunjun; Huang, Jing

doi:10.1007/s11063-021-10459-0

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…Thus, to solve this problem, we used an algorithm based on MDP and Q‐learning (QL) algorithm for estimating states (channel, user, and buffer states) and scheduling beams in MVNOs. The relay in an MVNO is programmed using QL algorithm and the relay continuously observe the operating environment to gather the information in order to obtain the optimal action based on its learned information . Finally, the learning algorithm finds the best possible optimized values for the MVNOs and their users.…”

Section: Solving An Mdp With Q‐learning (Ql)‐based Optimal Action For...mentioning

confidence: 99%

On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

Sapavath

Safavat

Rawat

2019

Trans Emerging Tel Tech

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In this paper, we study a machine learning enabled smart beam scheduling approach for wireless virtualization in large‐scale multiple‐input–multiple‐output (MIMO) system. Large‐scale MIMO is regarded as an emerging technology to enhance data rate of future wireless networks and the wireless virtualization is regarded as an efficient paradigm to enhance the radio frequency (RF) spectrum utilization by subleasing RF slices of wireless infrastructure providers to mobile virtual network operators (MVNOs). We leverage machine learning approach for scheduling the beams in large‐scale MIMO where RF slices with the help of subsets of antennas are subleased for MVNOs. Performance of the proposed approach is evaluated using simulation results. The results show that the proposed approach outperforms the state of the art approach.

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Section: Solving An Mdp With Q‐learning (Ql)‐based Optimal Action For...mentioning

confidence: 99%

On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

Sapavath

Safavat

Rawat

2019

Trans Emerging Tel Tech

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“…At present, deep neural network models are gradually being widely used in the field of image recognition because they can automatically extract complex features through the training of large amounts of data, without manual interven-tion, and have better prediction results for unknown data [2].Some studies have used CNN to extract features and classify tongue images, but the affine transformation robustness of CNN is poor, which can lead to the loss of spatial relationships between features. CapsNet proposed by Hinton [3] not only greatly reduces the size of the model but also more effectively utilizes spatial position information and better encodes the relationship between local information and global objectives.…”

Section: Introductionmentioning

confidence: 99%

Syndrome Types Classification Method of Skin Diseases Based on Tongue Hierarchical Feature Fusion

Wang,

Wang

et al. 2023

Preprint

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Tongue diagnosis is a non-invasive, painless diagnostic method by observing the tongue image of patients to diagnose and analyze their pathological conditions, which provides an opportunity for the future development of tongue diagnosis. However, the traditional tongue diagnosis method mainly relies on the experience and judgment of doctors, and is also easily affected by external factors. These factors hinder the development and application of tongue diagnosis. Currently, most studies use machine learning, which is time consuming and labor intensive. Other studies use deep learning based on convolutional neural network (CNN), but the affine transformation of CNN is less robust and easily loses the spatial relationship between features. In this work, we propose a traditional Chinese medicine (TCM) syndrome classification model of skin diseases based on tongue image hierarchical feature fusion. By adding a multi-scale residual module to the feature extraction part of the capsule network, we can extracted richer feature of tongue image. At the same time, the attention mechanism module is embedded in the multi-scale residual module, with the help of the attention mechanism module, the interference of tongue impurity information is suppressed, and accurate features are extracted for classification. Through experiments, it has been proven that our proposed method has achieved accuracy of 89.6\% in the classification of tongue for acne syndrome, and accuracy of 91.6\% in the dermatitis syndrome.

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Section: Introductionmentioning

confidence: 99%

TongueCaps: An Improved Capsule Network Model for Multi-Classification of Tongue Color

Yan

Jiang

2022

Diagnostics

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Tongue color is an important part of tongue diagnosis. The change of tongue color is affected by pathological state of body, blood rheology, and other factors. Therefore, physicians can understand a patient’s condition by observing tongue color. Currently, most studies use machine learning, which is time consuming and labor intensive. Other studies use deep learning based on convolutional neural network (CNN), but the affine transformation of CNN is less robust and easily loses the spatial relationship between features. Recently, Capsule Networks (CapsNet) have been proposed to overcome these problems. In our work, CapsNet is used for tongue color research for the first time, and improved model TongueCaps is proposed, which combines the advantage of CapsNet and residual block structure to achieve end to end tongue color classification. We conduct experiments on 1371 tongue images; TongueCaps achieve accuracy is 0.8456, sensitivity is 0.8474, and specificity is 0.9586. In addition, the size of TongueCaps is 8.11 M, and FLOPs is 1,335,342, which are smaller than CNN in comparison models. Experiments have confirmed that the CapsNet can be used for tongue color research, and improved model TongueCaps, in this paper, is superior to other comparison models in terms of accuracy, specificity and sensitivity, computational complexity, and size of model.

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RETRACTED ARTICLE: Burn Image Recognition of Medical Images Based on Deep Learning: From CNNs to Advanced Networks

Cited by 7 publications

References 25 publications

On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

Syndrome Types Classification Method of Skin Diseases Based on Tongue Hierarchical Feature Fusion

TongueCaps: An Improved Capsule Network Model for Multi-Classification of Tongue Color

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