Deep learning convolutional neural network in diagnosis of serous effusion in patients with malignant tumor by tomography

Zhang, Jiawen; Zhang, Zhiguo; Xu, Jianzhong; Ren, Weiqing; Cheng, Yufei; Wang, Chunan; Kan, Qingpeng

doi:10.1007/s11227-021-04051-5

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Bie et al [16] proposed a two-way gate unit recurrent neural network model of hierarchical attention mechanism for multiple-choice reading comprehension tasks, which introduced the hierarchical structure of documents so that context, questions, and candidates interacted at word and sentence level [16]. Zhang et al [17] improved the coding layer and reasoning layer in the previous machine reading comprehension model: vocabulary and syntax features were integrated into the coding layer, and self-matching of documents was realized in the reasoning layer, and a memory-based answer extraction network was proposed, which performed well in segment extraction tasks [17].…”

Section: Dlnn-related Research DL (Deep Learningmentioning

confidence: 99%

Evaluation of Ideological and Political Education Using Deep Learning Neural Networks

Sheng

Gao

2022

Mobile Information Systems

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The demand for talents extends beyond theoretical knowledge to include students’ IPE (Ideological and Political Education). We can only continue to promote the rapid development of university teaching in China by putting in place a solid management system. It is the foundation of college students’ IPE in this era of rapid information technology development to accurately adapt to and understand the actual problems and requirements of IPE proposed by the development of the times and innovate and develop accordingly. Deep learning (DL) is a sophisticated machine learning algorithm that outperforms previous technologies in speech and image recognition. In this paper, DLNN (deep learning neural network) technology is applied to IPE evaluation and a set of university-specific IPE evaluation index systems is developed. The BPNN (BP Neural Network) evaluation model is used to train and study a specific amount of teaching quality data in the MATLAB simulation tool. On the prediction of IPE evaluation, the BPNN algorithm has a prediction error of about 0.050.4 according to the findings. The validity and accuracy of using the BPNN algorithm to model IPE quality are demonstrated.

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Section: Dlnn-related Research DL (Deep Learningmentioning

confidence: 99%

Evaluation of Ideological and Political Education Using Deep Learning Neural Networks

Sheng

Gao

2022

Mobile Information Systems

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“…e fusion of convolutional neural networks greatly improves the accuracy of classi cation problems, especially the combination with a face recognition system, which can better distinguish the corresponding attributes of the face and design better optimization functions by obtaining more data. is paper is based on this and further improves the e ect of face recognition by using the Fisher criterion [2]. Figure 1 is a method and process of facial expression recognition based on a convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Face Recognition Method Using Fisher’s Criterion

Wang

2022

Mobile Information Systems

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In order to further improve the ability of automatic feature extraction of deep learning technology and better solve the problem of system recognition ability declining when the number of training samples is reduced or the number of iterations is reduced, this paper proposes a fisher-based convolutional neural network algorithm, which automatically obtains the structural feature information of a face image with the help of a deep learning algorithm network and then reduces the number of weights with the help of a convolutional neural network to further reduce the complexity of the face recognition model. Through the system test, the verification data shows that the recognition error rate can be controlled at about 10% by extracting 10 of the 14 pictures of each type of face for training. When the training images are reduced, the recognition rate can be further improved.

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“…The n input vectors ½x 1 , x 2 , ⋯, x n and their corresponding weight vectors ½w 1 , w 2 , ⋯, w n are used as the inner product, plus the bias b, and then, the output f ðx ; w, bÞ is obtained through the nonlinear activation function hð∑ i w i x i + bÞ. Convolutional neural network has the characteristics of local perception and weight sharing, which can reduce the number of training parameters and computational complexity [14].…”

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confidence: 99%

Automatic Modulation and Recognition of Robot Communication Signal Based on Deep Learning Neural Network

Zou

2022

Journal of Sensors

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In order to solve the problem that the traditional method of manually extracting expert features for communication signal recognition has large limitations and low accuracy under low signal-to-noise ratio, this paper proposes an automatic modulation and recognition method of robot communication signal based on deep learning neural network. In this method, the received signal is preprocessed to obtain the complex baseband signal including in-phase component and quadrature component. The signal is used as the data set of the input convolution neural network model. The model structure and the super parameters such as convolution kernel, step size, characteristic graph, and activation function are adjusted through multiple training, and the trained model is used to extract and recognize the features of the communication signal. It realizes the identification and classification of seven types of digital communication signals: 2FSK, 4FSK, BPSK, 8PSK, QPSK, QAM16, and QAM64. The experimental results show that the average recognition accuracy of the seven signals has reached 94.61% when the signal-to-noise ratio is 0 dB. Conclusion. The algorithm is proved to be effective and has high accuracy under the condition of low signal-to-noise ratio.

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Deep learning convolutional neural network in diagnosis of serous effusion in patients with malignant tumor by tomography

Cited by 6 publications

References 22 publications

Evaluation of Ideological and Political Education Using Deep Learning Neural Networks

Evaluation of Ideological and Political Education Using Deep Learning Neural Networks

Convolutional Neural Network Face Recognition Method Using Fisher’s Criterion

Automatic Modulation and Recognition of Robot Communication Signal Based on Deep Learning Neural Network

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