Three ResNet Deep Learning Architectures Applied in Pulmonary Pathologies Classification

Neili, Zakaria; Fezari, Mohamed; Redjati, Abdelghani; Sundaraj, Kenneth

doi:10.1109/ai-csp52968.2021.9671211

Cited by 24 publications

(2 citation statements)

References 23 publications

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“…RNN models adopt a residual learning methodology that significantly reduces the difficulty of the deep networks training process. Besides, RNN models have been widely applied in some research works, Zakaria, et al [7] utilized RNN models in the medical field to recognize and classify medical images. Li and Raim made optimization and improvement combined with the actual applications, which obtained very good results of over 98.2% accuracy for fruit leaves detection and recognition.…”

Section: Introductionmentioning

confidence: 99%

Deep Residual Neural Network for Efficient Traffic Sign Detection

Cai

et al. 2023

Proceedings of International Conference on Artificial Life and Robotics

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This paper established three deep residual neural network models with different architectures for traffic sign detection. Also, a new systematic analytic hierarchy process method for model performance evaluation has been proposed, which was utilized to determine the configuration of the deep learning model. In this paper, four evaluation metrics were used for analytic hierarchy process measurement, they are accuracy, stability, response time, and system capability. Based on the Tsinghua-Tencent 100K dataset, experimental results verified the feasibility of the proposed models for traffic sign detection and recognition which has training and testing accuracy of 99.03% and 98.01% respectively.

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Section: Introductionmentioning

confidence: 99%

Deep Residual Neural Network for Efficient Traffic Sign Detection

Cai

et al. 2023

Proceedings of International Conference on Artificial Life and Robotics

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“…He et al [ 22 ] proposed a Residual Network (ResNet) in 2016 to address the problem of gradient explosion after network model deepening, which can make the network model deeper. After the ResNet model was proposed, it has been widely applied in fields such as image recognition [ 23 , 24 ] and signal processing [ 25 ]. Jin and Kim proposed a PN code length estimation based on deep learning for the first time [ 26 ], using a basic CNN network to achieve PN code length estimation for direct spread spectrum signals with lengths of 16, 32, 64, and 128.…”

Section: Introductionmentioning

confidence: 99%

Period Estimation of Spread Spectrum Codes Based on ResNet

Gu,

Liu,

et al. 2023

Sensors

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In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from −20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.

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