Deep Residual Convolutional Neural Network Research Articles

Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering

AbstractThe noise within train is a paradox; while harmful to passenger health, it is useful to operators as it provides insights into the working status of vehicles and tracks. Recently, methods for identifying defects based on interior noise signals are emerging, among which representation learning is the foundation for deep neural network models to understand the key information and structure of the data. To provide foundational data for track fault detection, a representation learning framework for interior noise, named the interior noise representation framework, is introduced. The method includes: (i) using wavelet transform to represent the original noise signal and designing a soft and hard denoising module for dataset denoising; (ii) deep residual convolutional denoising variational autoencoder (VAE) module performs representation learning with a VAE and deep residual convolutional neural networks, enabling richer data augmentation for sparsely labeled samples by manipulating the embedding space; (iii) deep embedding clustering submodule balances the representation of reconstruction and clustering features through the joint optimization of these aspects, categorizing metro noise into three distinct classes and effectively discriminating significantly different features. The experimental results show that, compared to traditional mechanism‐based models for characterizing interior noise, this approach offers a data‐driven general analysis framework, providing a foundational model for downstream tasks.

Enhanced Deep Learning Approach for Electromagnetic Forward Modeling of Dielectric Target Within the Wide Frequency Band Using Deep Residual Convolutional Neural Network

Enhanced Deep Learning Approach for Electromagnetic Forward Modeling of Dielectric Target Within the Wide Frequency Band Using Deep Residual Convolutional Neural Network

ROI-Binarized Hyperbolic Region Segmentation and Characterization by Using Deep Residual Convolutional Neural Network with Skip Connection for GPR Imaging

ROI-Binarized Hyperbolic Region Segmentation and Characterization by Using Deep Residual Convolutional Neural Network with Skip Connection for GPR Imaging

A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion

In large-scale power systems, accurately detecting and diagnosing the type of faults when they occur in the grid is a challenging problem. The classification performance of most existing grid fault diagnosis methods depends on the richness and reliability of the data, in addition, it is difficult to obtain sufficient feature information from unimodal circuit signals. To address these issues, we propose a deep residual convolutional neural network (DRCNN)-based framework for grid fault diagnosis. First, we design a comprehensive information entropy value (CIEV) evaluation metric that combines fuzzy entropy (FuzEn) and mutual approximation entropy (MutEn) to integrate multiple decomposition subsequences. Then, DRCNN and heterogeneous graph transformer (HGT) are constructed for extracting multimodal features and considering modal variability. In addition, to obtain the implicit information of multimodal features and control the degree of their performance, we propose to incorporate the cross-modal attention fusion (CMAF) mechanism in the synthesis framework. We validate the proposed method on the three-phase transmission line dataset and VSB power line dataset with accuracies of 99.4 % and 99.0 %, respectively. The proposed method also achieves superior performance compared to classical and state-of-the-art methods.

Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks.

Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from ab initio to data-driven techniques. Although theoretical methods, such as time-dependent density functional theory (TD-DFT) calculations, have well-established physical relevance and are among the most popular methods in computational physics and chemistry, they exhibit errors that are inherent in their approximate nature. These high-throughput electronic structure calculations also incur a substantial computational cost. With the emergence of big-data initiatives, cost-effective, data-driven methods have gained traction, although their usability is highly contingent on the degree of data quality and sparsity. In this study, we present a workflow that employs deep residual convolutional neural networks (DR-CNN) and gradient boosting feature selection to predict peak optical absorption wavelengths (λmax) exclusively from SMILES representations of dye molecules and solvents; one would normally measure λmax using UV-vis absorption spectroscopy. We use a multifidelity modeling approach, integrating 34,893 DFT calculations and 26,395 experimentally derived λmax data, to deliver more accurate predictions via a Bayesian-optimized gradient boosting machine. Our approach is benchmarked against the state of the art that is reported in the scientific literature; results demonstrate that learnt representations via a DR-CNN workflow that is integrated with other machine learning methods can accelerate the design of molecules for specific optical characteristics.

IDRCNN

Early identification of pancreatic ductal adenocarcinoma (PDAC) improves prognosis. Still, it is difficult since lesions are generally smaller and difficult to define on contrast-enhanced computed tomography images (CE-CT). Ineffective PDAC diagnosis has recently been achieved using deep learning models, but the output localized and identified images are of poor quality. This research focuses on small lesions and presents a new, efficient automatic deep-learning network model for PDAC detection. The Improved Deep Residual Convolutional Neural Network (IDRCNN) detects PDAC. The hyperparameter is optimized using the Tunicate Swarm Optimization Algorithm (TSOA) algorithm. A better diagnosis is made due to segmenting the surrounding anatomy structure effects, such as PD. We train a proposed IDRCNN model for segmenting and detecting lesions automatically using CE-CT images. Two more IDRCNN models are trained with the aim of investigating the effects of anatomy integration: (i) segmentation of tumor and pancreas (IDRCNN_TP), and (ii) segmentation of pancreatic Duct (IDRCNN_PD). The three networks' performance was assessed using an external, publicly available test set. Due to its effective classification results, the proposed method produces improved identification results for automated preliminary diagnosis of PDAC in cervical cancer clinics and hospitals. The performance of the proposed method is evaluated using a publicly assessable CT image dataset. It outperforms the existing state-of-the-art methods and achieved 98.67% accuracy, 97.26% recall, 98.52% precision, 97.65% sensitivity, and 98.45% specificity for pancreatic tumor detection.

Design and experimental demonstration of underwater wireless optical communication system based on semantic communication paradigm.

Underwater wireless optical communication (UWOC) has been widely studied as a key technology for ocean exploration and exploitation. However, current UWOC systems neglect semantic information of transmitted symbols, leading to unnecessary consumption of communication resources for transmitting non-essential data. In this paper, we propose and demonstrate a deep-learning-based underwater wireless optical semantic communication (UWOSC) system for image transmission. By utilizing a deep residual convolutional neural network, the semantic information can be extracted and mapped into the transmitted symbols. Moreover, we design a channel model based on long short-term memory network and employ a two-phase training strategy to ensure that the system matches the underwater channel. To evaluate the performance of the proposed UWOSC system, we conduct a series of experiments on an emulated UWOC experimental platform, in which the effects of different turbidity channel environments and bandwidth compression ratios are investigated. Experimental results show that the UWOSC system exhibits superior performance compared to the conventional communication schemes, particularly in challenging channel environments and low bandwidth compression ratios.

A fine-tuning deep residual convolutional neural network for emotion recognition based on frequency-channel matrices representation of one-dimensional electroencephalography

Emotion recognition (ER) plays a crucial role in enabling machines to perceive human emotional and psychological states, thus enhancing human-machine interaction. Recently, there has been a growing interest in ER based on electroencephalogram (EEG) signals. However, due to the noisy, nonlinear, and nonstationary properties of electroencephalography signals, developing an automatic and high-accuracy ER system is still a challenging task. In this study, a pretrained deep residual convolutional neural network model, including 17 convolutional layers and one fully connected layer with transfer learning technique in combination frequency-channel matrices (FCM) of two-dimensional data based on Welch power spectral density estimate from the one-dimensional EEG data has been proposed for improving the ER by automatically learning the underlying intrinsic features of multi-channel EEG data. The experiment result shows a mean accuracy of 93.61 ± 0.84%, a mean precision of 94.70 ± 0.60%, a mean sensitivity of 95.13 ± 1.02%, a mean specificity of 91.04 ± 1.02%, and a mean F1-score of 94.91 ± 0.68%, respectively using 5-fold cross-validation on the DEAP dataset. Meanwhile, to better explore and understand how the proposed model works, we noted that the ranking of clustering effect of FCM for the same category by employing the t-distributed stochastic neighbor embedding strategy is: softmax layer activation is the best, the middle convolutional layer activation is the second, and the early max pooling layer activation is the worst. These findings confirm the promising potential of combining deep learning approaches with transfer learning techniques and FCM for effective ER tasks.

MResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI.

Brain tumor segmentation is an important direction in medical image processing, and its main goal is to accurately mark the tumor part in brain MRI. This study proposes a brand new end-to-end model for brain tumor segmentation, which is a multi-scale deep residual convolutional neural network called mResU-Net. The semantic gap between the encoder and decoder is bridged by using skip connections in the U-Net structure. The residual structure is used to alleviate the vanishing gradient problem during training and ensure sufficient information in deep networks. On this basis, multi-scale convolution kernels are used to improve the segmentation accuracy of targets of different sizes. At the same time, we also integrate channel attention modules into the network to improve its accuracy. The proposed model has an average dice score of 0.9289, 0.9277, and 0.8965 for tumor core (TC), whole tumor (WT), and enhanced tumor (ET) on the BraTS 2021 dataset, respectively. Comparing the segmentation results of this method with existing techniques shows that mResU-Net can significantly improve the segmentation performance of brain tumor subregions.

Research on image recognition of three Fritillaria cirrhosa species based on deep learning

Based on the deep learning method, a network model that can quickly and accurately identify the species of Fritillaria cirrhosa species was constructed. The learning method based on deep residual convolutional neural network was used to input the unprocessed original image directly as input, and the features of the image were extracted through convolution and pooling operations. On this basis, the ResNet34 model was improved, and the additional fully connected layer was added in front of the Softmax classifier to improve the learning ability of the network model. Total of 3915 images of three kinds of Fritillaria cirrhosa were used as data sources for the experiments, among which 160 images of each type were randomly selected to form the validation set. The final training set recognition accuracy rate was 95.8%, the validation set accuracy rate reached 92.3%, and the test set accuracy rate was 88.7%. The image recognition method of Fritillaria cirrhosa based on deep learning proposed in this paper is effective and feasible, which can quickly and accurately identify the species of Fritillaria cirrhosa species, and provides a new idea for the intelligent recognition of Chinese medicinal materials.

Deep residual convolutional neural Network: An efficient technique for intrusion detection system

The fast growth of computer networks over the past few years has made network security in smart cities a significant issue. Network intrusion detection is crucial to maintaining the integrity, confidentiality, and resource accessibility of the various network security rules. Conventional intrusion detection systems frequently use mining association rules to identify intrusion behaviors. They run into issues such as a high false alarm rate (FAR), limited generalization capacity, and slow timeliness because they cannot adequately extract distinctive information about user activities. The primary goal of the current research is to classify attacks using efficient approaches to identify genuine packets. If the number of characteristics in a dataset decreases, the complexity of DL approaches is significantly decreased. In this research work, the Deep Residual Convolutional neural network (DCRNN) is proposed to enhance network security through intrusion detection, which is optimized by the Improved Gazelle Optimization Algorithm (IGOA). Feature selection has eliminated irrelevant features from network data used in obstacle classification processes. Essential features are chosen using the Novel Binary Grasshopper Optimization Algorithm (NBGOA). Experimentation is carried out using the UNSW-NB-15, Cicddos2019 dataset, and CIC-IDS-2017 dataset. According to the experimental findings, the proposed system outperforms existing models regarding classification accuracy and processing time. The results demonstrate that the presented approach efficiently and precisely identifies various assaults.

Deep residual convolutional neural network based on hybrid attention mechanism for ecological monitoring of marine fishery

Understanding the ecological environment, population abundance, and growth status of marine organisms in the marine fishery is important to promote its sustainability. However, existing manual detection methods can cause some damage to marine ecology and are difficult to meet the demand for fast and accurate detection. In addition, light, shadows, and disturbances in the marine ecosystem can affect the effectiveness of intelligent detection methods. To address these problems, a deep residual convolutional neural network (DRCNN) based on hybrid attention mechanism (HAM) is proposed to detect marine organisms. The hybrid attention mechanism obtains key information from both channel and space dimensions of the input image. And the residual module is added to the deep convolutional neural network to iteratively extract image features while avoiding error accumulation. It is demonstrated experimentally that the HAM-DRCNN model achieves 93.17% image localization accuracy with a processing speed of 20.95 frames/s. Compared with the YOLOv5 and Faster R-CNN, the mean average precision of species classification is 91.36%, which is an improvement of 0.93% and 9.39%, respectively. In addition, excellent results are achieved on two other benchmark datasets. The method can accurately locate and complete the classification of marine organisms in underwater images and has practical application value.

CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK

In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, we proposed a technique to classify Parkinson’s disease by MRI brain images. Initially, normalize the input data using the min-max normalization method and then remove noise from input images using a median filter. Then utilizing the Binary Dragonfly Algorithm to select the features. Furthermore, to segment the diseased part from MRI brain images using the technique Dense-UNet. Then, classify the disease as if it’s Parkinson’s disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with Enhanced Whale Optimization Algorithm (EWOA) to get better classification accuracy. Here, we use the public Parkinson’s Progression Marker Initiative (PPMI) dataset for Parkinson’s MRI images. The accuracy, sensitivity, specificity, and precision metrics will be utilized with manually gathered data to assess the efficacy of the proposed methodology.

Automated classification of pediatric age from 12-lead ECG using deep residual convolutional neural networks

With increased interest in screening of young people for potential causes of sudden death, accurate automated detection of ventricular pre-excitation (VPE) or Wolff–Parkinson–White syndrome (WPW) in the pediatric resting ECG is important. Several recent studies have shown interobserver variability when reading screening ECGs and thus an accurate automated reading for this potential cause of sudden death is critical. We designed and tested an automated algorithm to detect pediatric VPE optimized for low prevalence.Digital ECGs with 12 leads or 15 leads (12-lead plus V3R, V4R and V7) were selected from multiple hospitals and separated into a testing and training database. Inclusion criterion was age less than 16 years. The reference for algorithm detection of VPE was cardiologist annotation of VPE for each ECG. The training database (n = 772) consisted of VPE ECGs (n = 37), normal ECGs (n = 492) and a high concentration of conduction defects, RBBB (n = 232) and LBBB (n = 11). The testing database was a random sample (n = 763). All ECGs were analyzed with the Philips DXL ECG Analysis algorithm for basic waveform measurements. Additional ECG features specific to VPE, mainly delta wave scoring, were calculated from the basic measurements and the average beat. A classifier based on decision tree bootstrap aggregation (tree bagger) was trained in multiple steps to select the number of decision trees and the 10 best features. The classifier accuracy was measured on the test database.The new algorithm detected pediatric VPE with a sensitivity of 78%, a specificity of 99.9%, a positive predictive value of 88% and negative predictive value of 99.7%.This new algorithm for detection of pediatric VPE performs well with a reasonable positive and negative predictive value despite the low prevalence in the general population.

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.

Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, hindering its further applications in radiation therapy. To restore low-quality CBCT images contaminated by scatter signals, a scatter correction algorithm combining the advantages of convolutional neural networks (CNN) and Swin Transformer is proposed. In this paper a scatter correction model for CBCT image, the Flip Swin Transformer U-shape network (FSTUNet) model, is proposed. In this model, the advantages of CNN in texture detail and Swin Transformer in global correlation are used to accurately extract shallow and deep features, respectively. Instead of using the original Swin Transformer tandem structure, we build the Flip Swin Transformer Block to achieve a more powerful inter-window association extraction. The validity and clinical relevance of the method is demonstrated through extensive experiments on a Monte Carlo (MC) simulation dataset and frequency split dataset generated by a validated method, respectively. Experimental results on the MC simulated dataset show that the root mean square error of images corrected by the method is reduced from over 100 HU to about 7 HU. Both the structural similarity index measure (SSIM) and the universal quality index (UQI) are close to 1. Experimental results on the frequency split dataset demonstrate that the method not only corrects shading artifacts but also exhibits a high degree of structural consistency. In addition, comparison experiments show that FSTUNet outperforms UNet, Deep Residual Convolutional Neural Network (DRCNN), DSENet, Pix2pixGAN, and 3DUnet methods in both qualitative and quantitative metrics. Accurately capturing the features at different levels is greatly beneficial for reconstructing high-quality scatter-free images. The proposed FSTUNet method is an effective solution to CBCT scatter correction and has the potential to improve the accuracy of CBCT image-guided radiation therapy.

A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia.

Schizophrenia is a severe brain disorder with serious symptoms including delusions, disorganized speech, and hallucinations that can have a long-term detrimental impact on different aspects of a patient's life. It is still unclear what the main cause of schizophrenia is, but a combination of altered brain connectivity and structure may play a role. Neuroimaging data has been useful in characterizing schizophrenia, but there has been very little work focused on voxel-wise changes in multiple brain networks over time, despite evidence that functional networks exhibit complex spatiotemporal changes over time within individual subjects. Recent studies have primarily focused on static (average) features of functional data or on temporal variations between fixed networks; however, such approaches are not able to capture multiple overlapping networks which change at the voxel level. In this work, we employ a deep residual convolutional neural network (CNN) model to extract 53 different spatiotemporal networks each of which captures dynamism within various domains including subcortical, cerebellar, visual, sensori-motor, auditory, cognitive control, and default mode. We apply this approach to study spatiotemporal brain dynamism at the voxel level within multiple functional networks extracted from a large functional magnetic resonance imaging (fMRI) dataset of individuals with schizophrenia (N = 708) and controls (N = 510). Our analysis reveals widespread group level differences across multiple networks and spatiotemporal features including voxel-wise variability, magnitude, and temporal functional network connectivity in widespread regions expected to be impacted by the disorder. We compare with typical average spatial amplitude and show highly structured and neuroanatomically relevant results are missed if one does not consider the voxel-wise spatial dynamics. Importantly, our approach can summarize static, temporal dynamic, spatial dynamic, and spatiotemporal dynamics features, thus proving a powerful approach to unify and compare these various perspectives. In sum, we show the proposed approach highlights the importance of accounting for both temporal and spatial dynamism in whole brain neuroimaging data generally, shows a high-level of sensitivity to schizophrenia highlighting global but spatially unique dynamics showing group differences, and may be especially important in studies focused on the development of brain-based biomarkers.

Enhanced Two-Step Deep-Learning Approach for Electromagnetic-Inverse-Scattering Problems: Frequency Extrapolation and Scatterer Reconstruction

The electromagnetic-inverse-scattering (EMIS) problem is solved by a novel two-step deep-learning (DL) approach in this article. The newly proposed two-step DL approach not only predicts the multifrequency EM scattered field, but also overcomes the limitation of the conventional methods for solving EMIS problems, such as expensive computational cost, strong ill-conditions, and invalidity on high contrast. In the first step, the complex-valued deep residual convolutional neural network (DRCNN) is utilized to predict multifrequency EM scattered fields only using single-frequency EM scattered field information. Based on a new complex-valued deep convolutional encoder-decoder (DCED) structure, the second step utilizes the obtained multifrequency EM scattered field “images” to realize the reconstruction of the target scatterers. In such a manner, the proposed approach can solve the EMIS problem accurately and efficiently even for inhomogeneous and high-contrast scatterers. The training of the proposed two DL models is based on the simple synthetic dataset. Numerical examples based on various dielectric objects are given to demonstrate the accuracy and performance of the newly proposed approach. The proposed DL-based method opens a new path for handling real-time quantitative microwave imaging.

A multiple-input deep residual convolutional neural network for reservoir permeability prediction

Permeability plays an essential role in reservoir-related studies, including fluid flow characterization, reservoir modeling/simulation, and management. However, operational constraints and high costs limit the wide access to the direct measurements of reservoir permeability. Over the years, various machine learning (ML) techniques have been utilized to predict reservoir permeability from widely-available reservoir-related datasets (such as well logs). Nevertheless, experience in real case studies reveals that there is still room to improve the process of permeability prediction in both well and field scales. In this study, an innovative Deep Learning (DL)-based approach is developed for fast and accurate reservoir permeability prediction from conventional well logs. By integrating a multiple-input module, convolutional network, deeper bottleneck, and residual structures, we developed a novel Multiple-Input deep Residual Convolutional Neural Network (named MIRes CNN) for this purpose. It is simultaneously fed by two different types of datasets, Numerical Well Logs (NWLs) and Graphical Feature Images (GFIs). To construct this MIRes CNN model, we integrated a Single-Input deep Residual one Dimensional CNN (SIRes 1D-CNN) to handle the NWL dataset and a Single-Input deep Residual two Dimensional CNN (SIRes 2D-CNN) to treat with GFIs. The significant advantages of the proposed architecture are its capability to efficiently exploit mixed data, reduce both overfitting problem and computational cost, and add more flexibility to the process compared to the classical single-input deep neural networks. The other novelty of this work lies in its generation of an image from geophysical well logs, which offers some physical insight. We compared the proposed MIRes CNN model with two Single-Input deep Residual CNN methods (i.e., SIRes 1D-CNN and SIRes 2D-CNN) and two baseline methods (i.e., Random Forest (RF) and Group Method of Data Handling (GMDH)). The statistical results concluded that the proposed multiple-input method outperforms single-modality-based techniques and the baseline methods for all employed performance indicators. The study also demonstrates the importance of using physically meaningful log-derived images for CNN model training and the crucial role played by the modified architectures (e.g., residual and deeper bottleneck architectures) in enhancing the accuracy and performance of the model.

ResNet for Histopathologic Cancer Detection, the Deeper, the Better?

Histopathological image classification has become one of the most challenging tasks among researchers due to the categories and fine-grained variability of the disease. Based on deep residual convolutional neural networks (ResNet), we study the classification tasks in medical image datasets. In this paper, we seek to answer the following central question in the context of medical image analysis: Is the deeper the network layer, the better the performance? Can the transfer of pre-trained deep ResNet with sufficient fine-tuning of all layers be better than the transfer with freezing most layers and training only the last layer? To address this question, ResNet with 18, 34, 50, 152-layers pre-trained from ImageNet were transferred in two different strategies on the histopathology image datasets of Kaggle, which included 220,025 histopathology patches with labels. The performance was also compared with traditional machine learning models. Our experiments consistently demonstrated that: (1) The deeper the ResNet network layers are, the better performance may not be (AUC value of ResNet-34 is 0.992 and ResNet-152 is 0.989). The performance should be determined by the richness of semantic features of the datasets but not the depth of the layers and the time and resources required for training should also be considered in model selection. (2) Transfer learning with deep ResNet (AUC: 0.881~0.992) works better than traditional machine learning with logistic regression (AUC: 0.775), which indicates the better generalization ability and robustness of the deep ResNet. (4) Freezing most layers of the deep ResNet and only train the last fully connected layer does not improve the accuracy and efficiency of transfer learning. Fine-tuning all-layers scheme offers a practical way to reach the best performance based on the amount of available data. (5) The performance of both transfer strategies depends largely on datasets. In conclusion, both strategies produce good results in terms of training time and overall accuracy when compared to models trained from scratch or traditional machine learning models.

Correction to “Quantized Deep Residual Convolutional Neural Network for Image-Based Dietary Assessment”

Correction to “Quantized Deep Residual Convolutional Neural Network for Image-Based Dietary Assessment”