Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images ...Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.展开更多
Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA...Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA)is critical in improving content delivered to end users.Convolutional neural networks(CNNs)used in IQA face two common challenges.One issue is that these methods fail to provide the best representation of the image.The other issue is that the models have a large number of parameters,which easily leads to overfitting.To address these issues,the dense convolution network(DSC-Net),a deep learning model with fewer parameters,is proposed for no-reference image quality assessment(NR-IQA).Moreover,it is obvious that the use of multimodal data for deep learning has improved the performance of applications.As a result,multimodal dense convolution network(MDSC-Net)fuses the texture features extracted using the gray-level co-occurrence matrix(GLCM)method and spatial features extracted using DSC-Net and predicts the image quality.The performance of the proposed framework on the benchmark synthetic datasets LIVE,TID2013,and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.展开更多
The stratigraphic correlation of well logs plays an essential role in characterizing subsurface reservoirs.However,it suffers from a small amount of training data and expensive computing time.In this work,we propose t...The stratigraphic correlation of well logs plays an essential role in characterizing subsurface reservoirs.However,it suffers from a small amount of training data and expensive computing time.In this work,we propose the Attention Based Dense Network(ASDNet)for the stratigraphic correlation of well logs.To implement the suggested model,we first employ the attention mechanism to the input well logs,which can effectively generate the weighted well logs to serve for further feature extraction.Subsequently,the DenseNet is utilized to achieve good feature reuse and avoid gradient vanishing.After model training,we employ the ASDNet to the testing data set and evaluate its performance based on the well log data set from Northwest China.Finally,the numerical results demonstrate that the suggested ASDNet provides higher prediction accuracy for automated stratigraphic correlation of well logs than state-of-the-art contrastive UNet and SegNet.展开更多
BACKGROUND The nature of input data is an essential factor when training neural networks.Research concerning magnetic resonance imaging(MRI)-based diagnosis of liver tumors using deep learning has been rapidly advanci...BACKGROUND The nature of input data is an essential factor when training neural networks.Research concerning magnetic resonance imaging(MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing.Still,evidence to support the utilization of multi-dimensional and multi-parametric image data is lacking.Due to higher information content,three-dimensional input should presumably result in higher classification precision.Also,the differentiation between focal liver lesions(FLLs)can only be plausible with simultaneous analysis of multisequence MRI images.AIM To compare diagnostic efficiency of two-dimensional(2D)and three-dimensional(3D)-densely connected convolutional neural networks(DenseNet)for FLLs on multi-sequence MRI.METHODS We retrospectively collected T2-weighted,gadoxetate disodium-enhanced arterial phase,portal venous phase,and hepatobiliary phase MRI scans from patients with focal nodular hyperplasia(FNH),hepatocellular carcinomas(HCC)or liver metastases(MET).Our search identified 71 FNH,69 HCC and 76 MET.After volume registration,the same three most representative axial slices from all sequences were combined into four-channel images to train the 2D-DenseNet264 network.Identical bounding boxes were selected on all scans and stacked into 4D volumes to train the 3D-DenseNet264 model.The test set consisted of 10-10-10 tumors.The performance of the models was compared using area under the receiver operating characteristic curve(AUROC),specificity,sensitivity,positive predictive values(PPV),negative predictive values(NPV),and f1 scores.RESULTS The average AUC value of the 2D model(0.98)was slightly higher than that of the 3D model(0.94).Mean PPV,sensitivity,NPV,specificity and f1 scores(0.94,0.93,0.97,0.97,and 0.93)of the 2D model were also superior to metrics of the 3D model(0.84,0.83,0.92,0.92,and 0.83).The classification metrics of FNH were 0.91,1.00,1.00,0.95,and 0.95 using the 2D and 0.90,0.90,0.95,0.95,and 0.90 using the 3D models.The 2D and 3D networks'performance in the diagnosis of HCC were 1.00,0.80,0.91,1.00,and 0.89 and 0.88,0.70,0.86,0.95,and 0.78,respectively;while the evaluation of MET lesions resulted in 0.91,1.00,1.00,0.95,and 0.95 and 0.75,0.90,0.94,0.85,and 0.82 using the 2D and 3D networks,respectively.CONCLUSION Both 2D and 3D-DenseNets can differentiate FNH,HCC and MET with good accuracy when trained on hepatocyte-specific contrast-enhanced multi-sequence MRI volumes.展开更多
In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing...In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing a densely connected convolutional neural network(D-CNN)model to demodulate the full spectrum.The proposedπ-PSTFBG sensor is prepared by using the advanced fiber grating inscription system based on a two-beam interferometry method,which could introduce deeper features of dip-splitting for all the lossy dips in the spectrum,giving the possibility of fully measuring the change of RI.This enhanced feature gives relatively higher prediction accuracy(R^(2) of 99.67%)using the well-trained D-CNN model compared with the results achieved by pure TFBG or that with a gold coating.As a further demonstration from a practical view,a prototype integrated with the proposed D-CNN algorithm is developed to conduct RI measurement of NaCl solutions in real time using aπ-PSTFBG-based RI sensor.The results show that the proposed real-time demodulation system is capable of measuring RI with an average error of 1.6×10^(-4)RIU in a short response time of<1 s.The demonstrated spectral demodulation approach powered by deep learning shows great potential in real-time analysis for chemical solutions and point-of-care medical testing based on RI changes,especially for the portable requirements.展开更多
Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement ...Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement method with deep Convolutional Neural Networks(CNN)for compression and reconstruction of the image.The proposed method does color and contrast correction for image enhancement.The enhanced images are down-sampled using 9-layer CNN followed by Discrete Wavelet Transform(DWT).The decompression is done by using Inverse DWT.Further,the sub-pixel up-sampled image is de-blurred using a three-layer CNN.Residual Dense CNN(RD-CNN)is used to improve the quality of the reconstructed image after deblurring.The quality of the reconstructed images is measured using Peak Signal to Noise Ratio(PSNR)and Structural Similarity Index Metric(SSIM).The proposed model provides better image enhancement,compression,and reconstruction quality than the existing state-of-the-art methods and Super Resolution CNN(SRCNN)respectively.展开更多
文摘Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.
文摘Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA)is critical in improving content delivered to end users.Convolutional neural networks(CNNs)used in IQA face two common challenges.One issue is that these methods fail to provide the best representation of the image.The other issue is that the models have a large number of parameters,which easily leads to overfitting.To address these issues,the dense convolution network(DSC-Net),a deep learning model with fewer parameters,is proposed for no-reference image quality assessment(NR-IQA).Moreover,it is obvious that the use of multimodal data for deep learning has improved the performance of applications.As a result,multimodal dense convolution network(MDSC-Net)fuses the texture features extracted using the gray-level co-occurrence matrix(GLCM)method and spatial features extracted using DSC-Net and predicts the image quality.The performance of the proposed framework on the benchmark synthetic datasets LIVE,TID2013,and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.
基金supported by the Key Research and Development Program of Shaanxi,China under Grant 2023-YBGY-076the Fundamental Research Funds for the Central Universities,China under Grant XZY012022086the China Postdoctoral Science Foundation Project under Grant 2022M712509.
文摘The stratigraphic correlation of well logs plays an essential role in characterizing subsurface reservoirs.However,it suffers from a small amount of training data and expensive computing time.In this work,we propose the Attention Based Dense Network(ASDNet)for the stratigraphic correlation of well logs.To implement the suggested model,we first employ the attention mechanism to the input well logs,which can effectively generate the weighted well logs to serve for further feature extraction.Subsequently,the DenseNet is utilized to achieve good feature reuse and avoid gradient vanishing.After model training,we employ the ASDNet to the testing data set and evaluate its performance based on the well log data set from Northwest China.Finally,the numerical results demonstrate that the suggested ASDNet provides higher prediction accuracy for automated stratigraphic correlation of well logs than state-of-the-art contrastive UNet and SegNet.
文摘BACKGROUND The nature of input data is an essential factor when training neural networks.Research concerning magnetic resonance imaging(MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing.Still,evidence to support the utilization of multi-dimensional and multi-parametric image data is lacking.Due to higher information content,three-dimensional input should presumably result in higher classification precision.Also,the differentiation between focal liver lesions(FLLs)can only be plausible with simultaneous analysis of multisequence MRI images.AIM To compare diagnostic efficiency of two-dimensional(2D)and three-dimensional(3D)-densely connected convolutional neural networks(DenseNet)for FLLs on multi-sequence MRI.METHODS We retrospectively collected T2-weighted,gadoxetate disodium-enhanced arterial phase,portal venous phase,and hepatobiliary phase MRI scans from patients with focal nodular hyperplasia(FNH),hepatocellular carcinomas(HCC)or liver metastases(MET).Our search identified 71 FNH,69 HCC and 76 MET.After volume registration,the same three most representative axial slices from all sequences were combined into four-channel images to train the 2D-DenseNet264 network.Identical bounding boxes were selected on all scans and stacked into 4D volumes to train the 3D-DenseNet264 model.The test set consisted of 10-10-10 tumors.The performance of the models was compared using area under the receiver operating characteristic curve(AUROC),specificity,sensitivity,positive predictive values(PPV),negative predictive values(NPV),and f1 scores.RESULTS The average AUC value of the 2D model(0.98)was slightly higher than that of the 3D model(0.94).Mean PPV,sensitivity,NPV,specificity and f1 scores(0.94,0.93,0.97,0.97,and 0.93)of the 2D model were also superior to metrics of the 3D model(0.84,0.83,0.92,0.92,and 0.83).The classification metrics of FNH were 0.91,1.00,1.00,0.95,and 0.95 using the 2D and 0.90,0.90,0.95,0.95,and 0.90 using the 3D models.The 2D and 3D networks'performance in the diagnosis of HCC were 1.00,0.80,0.91,1.00,and 0.89 and 0.88,0.70,0.86,0.95,and 0.78,respectively;while the evaluation of MET lesions resulted in 0.91,1.00,1.00,0.95,and 0.95 and 0.75,0.90,0.94,0.85,and 0.82 using the 2D and 3D networks,respectively.CONCLUSION Both 2D and 3D-DenseNets can differentiate FNH,HCC and MET with good accuracy when trained on hepatocyte-specific contrast-enhanced multi-sequence MRI volumes.
基金National Natural Science Foundation of China(62275277,U2001601)Guangdong Project(2021QN02X055)+3 种基金Guangdong ST Programme(2024B0101030001)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(23lgbj007)Science and Technology Planning Project of Guangzhou(2024A04J9891)Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2023SP231)。
文摘In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing a densely connected convolutional neural network(D-CNN)model to demodulate the full spectrum.The proposedπ-PSTFBG sensor is prepared by using the advanced fiber grating inscription system based on a two-beam interferometry method,which could introduce deeper features of dip-splitting for all the lossy dips in the spectrum,giving the possibility of fully measuring the change of RI.This enhanced feature gives relatively higher prediction accuracy(R^(2) of 99.67%)using the well-trained D-CNN model compared with the results achieved by pure TFBG or that with a gold coating.As a further demonstration from a practical view,a prototype integrated with the proposed D-CNN algorithm is developed to conduct RI measurement of NaCl solutions in real time using aπ-PSTFBG-based RI sensor.The results show that the proposed real-time demodulation system is capable of measuring RI with an average error of 1.6×10^(-4)RIU in a short response time of<1 s.The demonstrated spectral demodulation approach powered by deep learning shows great potential in real-time analysis for chemical solutions and point-of-care medical testing based on RI changes,especially for the portable requirements.
文摘Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement method with deep Convolutional Neural Networks(CNN)for compression and reconstruction of the image.The proposed method does color and contrast correction for image enhancement.The enhanced images are down-sampled using 9-layer CNN followed by Discrete Wavelet Transform(DWT).The decompression is done by using Inverse DWT.Further,the sub-pixel up-sampled image is de-blurred using a three-layer CNN.Residual Dense CNN(RD-CNN)is used to improve the quality of the reconstructed image after deblurring.The quality of the reconstructed images is measured using Peak Signal to Noise Ratio(PSNR)and Structural Similarity Index Metric(SSIM).The proposed model provides better image enhancement,compression,and reconstruction quality than the existing state-of-the-art methods and Super Resolution CNN(SRCNN)respectively.
