A Time-Distributed Convolutional Long Short-Term Memory for Hand Gesture Recognition

Serj, Mehdi Fatan; Asgari, Mersad; Lavi, Bahram; Valls, Domenec Puig; García, Miguel Ángel

doi:10.1109/icee52715.2021.9544445

Cited by 3 publications

(2 citation statements)

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“…This method can automatically capture the target area after training on a large number of data sets ( Litjens et al, 2017 ; Chan H. P. et al, 2020 ). Therefore, DL is widely used in medical image processing such as classification between tumor epithelium and stroma ( Du et al, 2018 ), providing new prognostic biomarkers for cancer recurrence prediction ( Wang et al, 2016 ), computer-aided diagnosis (CAD) of prostate cancer and lung cancer ( Serj et al, 2018 ; Song et al, 2018 ), automatic segmentation of the left ventricle in echocardiographic images ( Kim et al, 2021 ), and classification of benign and malignant breast tumor and quality inspection in mammography images ( Borges Sampaio et al, 2011 ; Rouhi et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based automatic staging method for early endometrial cancer on MRI images

et al. 2022

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Early treatment increases the 5-year survival rate of patients with endometrial cancer (EC). Deep learning (DL) as a new computer-aided diagnosis method has been widely used in medical image processing which can reduce the misdiagnosis by radiologists. An automatic staging method based on DL for the early diagnosis of EC will benefit both radiologists and patients. To develop an effective and automatic prediction model for early EC diagnosis on magnetic resonance imaging (MRI) images, we retrospectively enrolled 117 patients (73 of stage IA, 44 of stage IB) with a pathological diagnosis of early EC confirmed by postoperative biopsy at our institution from 1 January 2018, to 31 December 2020. Axial T2-weighted image (T2WI), axial diffusion-weighted image (DWI) and sagittal T2WI images from 117 patients have been classified into stage IA and stage IB according to the patient’s pathological diagnosis. Firstly, a semantic segmentation model based on the U-net network is trained to segment the uterine region and the tumor region on the MRI images. Then, the area ratio of the tumor region to the uterine region (TUR) in the segmentation map is calculated. Finally, the receiver operating characteristic curves (ROCs) are plotted by the TUR and the results of the patient’s pathological diagnosis in the test set to find the optimal staging thresholds for stage IA and stage IB. In the test sets, the trained semantic segmentation model yields the average Dice similarity coefficients of uterus and tumor on axial T2WI, axial DWI, and sagittal T2WI were 0.958 and 0.917, 0.956 and 0.941, 0.972 and 0.910 respectively. With pathological diagnostic results as the gold standard, the classification model on axial T2WI, axial DWI, and sagittal T2WI yielded an area under the curve (AUC) of 0.86, 0.85 and 0.94, respectively. In this study, an automatic DL-based segmentation model combining the ROC analysis of TUR on MRI images presents an effective early EC staging method.

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Section: Introductionmentioning

confidence: 99%

A deep learning-based automatic staging method for early endometrial cancer on MRI images

et al. 2022

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“…In Polycarpou (1996), the main framework in the field of identification and control of nonlinear systems, which is a parametric method for adaptive nonlinear and linear control in order to achieve stability, is proposed. In Kumar et al (2017) and Serj et al (2021), the behavior of recurrent neural networks (RNNs) is investigated, and their stability as a controller is proven. Shen et al (2019) have looked at the cooperative control problem in nonlinear multi-agent systems using an NN-based adaptive control method.…”

Section: Introductionmentioning

confidence: 99%

Feedback error learning neural network for stable control of nonlinear nonaffine systems

Asgari

2022

Transactions of the Institute of Measurement and Control

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This study presents a stable feedback error learning (FEL) scheme for nonlinear nonaffine systems in the presence of uncertainty and disturbances. The distinguishing feature of the FEL method, which can have a significant effect on both transient and steady-state performance, has led us to adopt this approach. The nonlinear system studied here is nonaffine. In other words, the function that describes the dynamic equations of the system is an implicit function of the control input rather than a particular class of systems. We aim to develop a stable FEL control system with three components: a neural network (NN), a linear controller, and a robustifying control term. To this end, all the adaptation laws for the NN weights are derived from a Lyapunov function, ensuring that the closed-loop system is uniformly asymptotically stable (UAS). Thus, an NN learning control approach that effectively improves the transient performance, as well as the steady-state performance, is proposed, and its remarkable effectiveness is illustrated in comparison with the existing methods.

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Surface Electromyography based Wrong Posture Muscle Detector

Melkish,

Joy,

EphraimImmanuel

et al. 2023

2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)

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A Time-Distributed Convolutional Long Short-Term Memory for Hand Gesture Recognition

Cited by 3 publications

References 16 publications

A deep learning-based automatic staging method for early endometrial cancer on MRI images

A deep learning-based automatic staging method for early endometrial cancer on MRI images

Feedback error learning neural network for stable control of nonlinear nonaffine systems

Surface Electromyography based Wrong Posture Muscle Detector

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