Deep Residual Network Research Articles

Automatic assessment of nailfold capillaroscopy software: a pilot study

IntroductionCapillaroscopy is a simple method of nailfold capillary imaging, used to diagnose diseases from the systemic sclerosis spectrum. However, the assessment of the capillary image is time-consuming and subjective. This makes it difficult to use for a detailed comparison of studies assessed by various physicians. This pilot study aimed to validate software used for automatic capil­lary counting and image classification as normal or pathological.Material and methodsThe study was based on the assessment of 200 capillaroscopic images obtained from patients suffering from systemic sclerosis or scleroderma spectrum diseases and healthy people. Dinolite MEDL4N Pro was used to perform capillaroscopy. Each image was analysed manually and described using working software. The neural network was trained using the fast.ai library (based on PyTorch). The ResNet-34 deep residual neural network was chosen; 10-fold cross-validation with the validation and test set was performed, using the Darknet-YoloV3 state of the art neural network in a GPU-optimized (P5000 GPU) environment. For the calculation of 1 mm capillaries, an additional detection mechanism was designed.ResultsThe results obtained under neural network training were compared to the results obtained in manual analysis. The sensitivity of the automatic tool relative to manual assessment in classification of correct vs. pathological images was 89.0%, specificity 89.4% for the training group, in validation 89.0% and 86.9% respectively. For the average number of capillaries in 1 mm the precision of real images detected within the region of interest was 96.48%.ConclusionsThe pilot software for fully automatic capillaroscopic image assessment can be a useful tool for the rapid classification of a normal and altered capillaroscopy pattern. In addition, it allows one to quickly calculate the number of capillaries. In the future, the tool will be developed and will make it possible to obtain full imaging characteristics independent of the experience of the examiner.

A novel epilepsy detection approach using intrinsic multiscale entropy analysis and DSEAM-enhanced 1D-ResNets

Abstract Epilepsy, a prevalent neurological disorder, typically requires a complex diagnostic process involving medical history inquiry, physical examination, head computed tomography, and electroencephalogram (EEG) visual examination. Among the existing epilepsy automated detection algorithms, machine learning methods require manual feature selection. Most of deep learning algorithms for automatic detection have high complexity and computational complexity. To address this issue, this study proposes a high-precision, robust, and low computational epilepsy automatic detection algorithm based on EEG signal processing. The study utilizes ensemble empirical mode decomposition to preprocess the original EEG signal, breaking it down into intrinsic mode functions (IMFs) across various frequency bands. These IMFs contain information about epilepsy occurrence within the signal at different frequency bands. To enhance computational efficiency and reduce data dimension, the refined composite multiscale dispersion entropy of each IMF is further computed at different scales, referred to as intrinsic multiscale entropy (IME) analysis. IME analysis consolidates epilepsy occurrence information from EEG signals across different frequency bands and scales, linking entropy values to generate feature vectors. Drawing inspiration from successful deep residual networks and Squeeze-and-Excitation networks, the study introduces a double Squeeze-and-Excitation attention module enhanced one-dimensional residual network to classify one-dimensional feature vectors. The proposed method was tested on epilepsy dataset from University of Bonn, and the results demonstrated superior classification performance. In the experiment, the distinction between normal and epileptic EEG signals achieved 100% accuracy rate, while distinguishing between normal, epileptic interval, and epileptic EEG signals achieved accuracy rate of 99.41%.

DRN-DSA: A Hybrid Deep Learning Network Model for Precipitation Nowcasting using Time Series Data

Precipitation nowcasting involves short-term weather forecasting, predicting rain or snow within the next two hours. By analyzing current atmospheric conditions, it aids meteorologists in identifying weather patterns and preparing for severe events such as flooding. These nowcasts are typically displayed on geographical maps by weather services. However, the rapidly changing climate conditions make precipitation nowcasting a formidable challenge, as accurate short-term forecasts are hindered by immediate weather fluctuations. Traditional nowcasting methods, like numerical models and radar extrapolation, have limitations in delivering highly detailed and timely precipitation nowcasts. To overcome this issue, an effective solution is framed for precipitation nowcasting using a hybrid network approach named Deep Residual Network-Deep Stacked Autoencoder (DRN-DSA). Initially, the input time series data is acquired from the dataset. Thereafter, the effective technical indicators are extracted at the feature extraction stage. Later on, precipitation-type nowcasting is carried out using the proposed hybrid DRN-DSA, which is developed by incorporating a Deep Stacked Autoencoder (DSA) and Deep Residual Network (DRN). Finally, Weather nowcasting is carried out using the same proposed hybrid DSA-DRN. Moreover, when compared to other traditional models, the proposed DRN-DSA has gained superior results with a Relative Absolute Error (RAE) of 0.295, Root Mean Square Error (RMSE) of 0.154, low Mean Square Error (MSE) of 0.0236, Mean Absolute Percentage Error (MAPE) of 0.295, and False Acceptance Rate (FAR) of 0.0118.

Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification.

In response to increasing data privacy regulations, this work examines the use of federated learning for deep residual networks to diagnose cardiac abnormalities from electrocardiogram (ECG) data. This approach allows medical institutions to collaborate without exchanging raw patient data. We utilize the publicly available data from the PhysioNet/Computing in Cardiology Challenge 2021, featuring diverse ECG databases, to compare the classification performance of three federated learning methods against both central training with data sharing and isolated training scenarios. We show that federated learning outperforms ECG classifiers trained in isolation. In particular, our findings demonstrate that a globally trained model fine-tuned to specific local datasets surpasses non-collaborative approaches. This shows that models trained in federation learn general features that can be tailored to specific tasks. Furthermore, federated learning almost matches the performance of central training with data sharing on out-of-distribution data from non-participating institutions. These results highlight the ability of federated learning in developing models that generalize well across diverse patient data, without the need to share data among institutions, thus addressing data privacy concerns.

Dual-path aggregation transformer network for super-resolution with images occlusions and variability

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.

Predicting an opaque bubble layer during small-incision lenticule extraction surgery based on deep learning.

This study aimed to predict the formation of OBL during femtosecond laser SMILE surgery by employing deep learning technology. This was a cross-sectional, retrospective study conducted at a university hospital. Surgical videos were randomly divided into a training (3,271 patches, 73.64%), validation (704 patches, 15.85%), and internal verification set (467 patches, 10.51%). An artificial intelligence (AI) model was developed using a SENet-based residual regression deep neural network. Model performance was assessed using the mean absolute error (E MA ), Pearson's correlation coefficient (r), and determination coefficient (R 2 ). Four distinct types of deep neural network models were established. The modified deep residual neural network prediction model with channel attention built on the PyTorch framework demonstrated the best predictive performance. The predicted OBL area values correlated well with the Photoshop-based measurements (E MA = 0.253, r = 0.831, R 2 = 0.676). The ResNet (E MA = 0.259, r = 0.798, R 2 = 0.631) and Vgg19 models (E MA = 0.31, r = 0.758, R 2 = 0.559) both displayed satisfactory predictive performance, while the U-net model (E MA = 0.605, r = 0.331, R 2 = 0.171) performed poorest. We used a panoramic corneal image obtained before the SMILE laser scan to create a unique deep residual neural network prediction model to predict OBL formation during SMILE surgery. This model demonstrated exceptional predictive power, suggesting its clinical applicability across a broad field.

Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network.

Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.

Diabetic Retinopathy Classification Using Deep Residual Network with Remora Tuna Swarm Optimization

Diabetic Retinopathy Classification Using Deep Residual Network with Remora Tuna Swarm Optimization

Hybrid Feature Extraction for Breast Cancer Classification Using the Ensemble Residual VGG16 Deep Learning Model

Introduction: Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain a disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy and efficiency of breast cancer classification. However, tumor features' complexity and imaging data variability still pose challenges. Method: This study proposes the Ensemble Residual-VGG-16 model as a novel combination of the Deep Residual Network (DRN) and VGG-16 architecture. This model is purposely engineered with maximal precision for the task of breast cancer diagnosis based on mammography images. We assessed its performance by accuracy, recall, precision, and the F1-Score. All these metrics indicated the high performance of this Residual-VGG-16 model. The diagnostic residual-VGG16 performed exceptionally well with an accuracy of 99.6%, precision of 99.4%, recall of 99.7%, F1 score of 98.6%, and Mean Intersection over Union (MIoU) of 99.8% with MIAS datasets. Result: Similarly, the INBreast dataset achieved an accuracy of 93.8%, a precision of 94.2%, a recall of 94.5%, and an F1-score of 93.4% Conclusion: The proposed model is a significant advancement in breast cancer diagnosis, with high accuracy and potential as an automated grading.

Hybrid control for capacitor-assisted Z-source inverter in grid-connected photovoltaic system

Renewable energy integration into the power grid is essential for sustainable energy systems, but maintaining efficiency and reliability in such systems remains a key challenge. This study introduces a novel hybrid control topology for a Capacitor-Assisted Extended Boost Z-Source Multilevel Inverter in a grid associated solar photovoltaic (PV) structure. The suggested hybrid technique implies a united implementation of a Gannet Optimization Algorithm and Spiking Deep Residual Network and usually referred as GOA-SDRN technique. Initially, the modelling of the inverter is collected to get the best signal by the proposed controller. The proposed inverter configuration consists of a minimal quantity of diodes, switches, and sources. This configuration also offers advantages like less total harmonic distortion (THD) and reduced electromagnetic interference, making it a favourable choice for PV integration. The GOA is employed to determine the most favourable gain limiting factor based on a variation of power from their regular values. This control method ideally satisfies the load demand while reducing changes in the system limiting factor and external disturbances. The proposed control topology is executed in MATLAB and the concept is contrasted to different techniques. The THD values of existing methods such as Particle Swarm Optimization, Genetic Algorithm, and Grasshopper Optimization Algorithm are 1.36 %,0.89 %, and 1.99 % respectively, while the THD value of the proposedmethod is 0.63 %, demonstrating its optimal performance over existing methods. The proposed GOA-SDRN technique provides an effective solution for enhancing inverter efficiency and reducing distortion in grid-associated PV systems.

Four-phase CT lesion recognition based on multi-phase information fusion framework and spatiotemporal prediction module

Multiphase information fusion and spatiotemporal feature modeling play a crucial role in the task of four-phase CT lesion recognition. In this paper, we propose a four-phase CT lesion recognition algorithm based on multiphase information fusion framework and spatiotemporal prediction module. Specifically, the multiphase information fusion framework uses the interactive perception mechanism to realize the channel-spatial information interactive weighting between multiphase features. In the spatiotemporal prediction module, we design a 1D deep residual network to integrate multiphase feature vectors, and use the GRU architecture to model the temporal enhancement information between CT slices. In addition, we employ CT image pseudo-color processing for data augmentation and train the whole network based on a multi-task learning framework. We verify the proposed network on a four-phase CT dataset. The experimental results show that the proposed network can effectively fuse the multi-phase information and model the temporal enhancement information between CT slices, showing excellent performance in lesion recognition.

Real-Time Resolution Enhancement of Confocal Laser Scanning Microscopy via Deep Learning

Confocal laser scanning microscopy is one of the most widely used tools for high-resolution imaging of biological cells. However, the imaging resolution of conventional confocal technology is limited by diffraction, and more complex optical principles and expensive optical-mechanical structures are usually required to improve the resolution. This study proposed a deep residual neural network algorithm that can effectively improve the imaging resolution of the confocal microscopy in real time. The reliability and real-time performance of the algorithm were verified through imaging experiments on different biological structures, and an imaging resolution of less than 120 nm was achieved in a more cost-effective manner. This study contributes to the real-time improvement of the imaging resolution of confocal microscopy and expands the application scenarios of confocal microscopy in biological imaging.

StructNet-DDI: Molecular Structure Characterization-Based ResNet for Prediction of Drug-Drug Interactions.

This study introduces a deep learning framework based on SMILES representations of chemical structures to predict drug-drug interactions (DDIs). The model extracts Morgan fingerprints and key molecular descriptors, transforming them into raw graphical features for input into a modified ResNet18 architecture. The deep residual network, enhanced with regularization techniques, efficiently addresses training issues such as gradient vanishing and exploding, resulting in superior predictive performance. Experimental results show that StructNet-DDI achieved an AUC of 99.7%, an accuracy of 94.4%, and an AUPR of 99.9%, demonstrating the model's effectiveness and reliability. These findings highlight that StructNet-DDI can effectively extract crucial features from molecular structures, offering a simple yet robust tool for DDI prediction.

Multi-Source Information-Based Bearing Fault Diagnosis Using Multi-Branch Selective Fusion Deep Residual Network.

Rolling bearing is the core component of industrial machines, but it is difficult for common single signal source-based fault diagnosis methods to ensure reliable results since sensor signals are vulnerable to the pollution of background noises and the attenuation of transmitted information. Recently, multi-source information-based fault diagnosis methods have become popular, but the information redundancy between multiple signals is a tough problem that will negatively impact the representational capacity of deep learning algorithms and the precision of fault diagnosis methods. Besides that, the characteristics of various signals are actually different, but this problem was usually omitted by researchers, and it has potential to further improve the diagnosing performance by adaptively adjusting the feature extraction process for every input signal source. Aimed at solving the above problems, a novel model for bearing fault diagnosis called multi-branch selective fusion deep residual network is proposed in this paper. The model adopts a multi-branch structure design to enable every input signal source to have a unique feature processing channel, avoiding the information of multiple signal sources blindly coupled by convolution kernels. And in each branch, different convolution kernel sizes are assigned according to the characteristics of every input signal, fully digging the precious fault components on respective information sources. Lastly, the dropout technique is used to randomly throw out some activated neurons, alleviating the redundancy and enhancing the quality of the multiscale features extracted from different signals. The proposed method was experimentally compared with other intelligent methods on two authoritative public bearing datasets, and the experimental results prove the feasibility and superiority of the proposed model.

Pathological voice detection using optimized deep residual neural network and explainable artificial intelligence

Pathological voice detection using optimized deep residual neural network and explainable artificial intelligence

Sequential Recommendation Model Based on Deep Residual Recurrent Neural Network

In order to solve the problem that the sequence recommendation model based on RNN (Recurrent Neural Network) is prone to gradient vanishing or exploding when processing long sequences, which leads to the instability of the recommendation model training process, residual connection, layer normalization and feedforward neural network modules are introduced on the basis of the traditional gated recurrent unit (GRU), and a sequence recommendation model DeepGRU based on deep residual recurrent neural network is proposed. It is verified on 3 public datasets. The experimental results show that the DeepGRU has obvious advantages over the most advanced sequence recommendation methods (the average recommendation accuracy is improved 8.68%). The ablation experiment verifies the effectiveness of the introduced residual connection and other modules under the DeepGRU framework. In addition, the DeepGRU effectively alleviates the problem of unstable training process when processing long sequences.

Road target recognition based on radar range-Doppler spectrum with GS – ResNet

ABSTRACT Road target recognition ability of automotive millimetre-wave (mmWave) radar is crucial in areas such as autonomous driving, advanced driver assistance systems (ADAS), and automated emergency braking systems (AEBS). Current mmWave radar systems are primarily utilized for speed, distance, and angle measurements, with limited capability or unsatisfactory performance in target classification. In response to this limitation, this paper proposes a novel method for road target recognition, using radar range-Doppler spectrum as input data and introducing a global attention mechanism and SCConv module. First, the range-Doppler spectrum is processed by the clutter removal algorithm block to remove background clutter and vibration interference. Second, considering the small features and susceptibility to noise interference in the range-Doppler spectrum, we have devised a deep residual network based on a global attention mechanism, significantly enhancing the model’s accuracy in range-Doppler spectrum classification. Finally, we introduce the SCConv module and improve the downsampling module in ResNet to enhance the model’s classification accuracy. Experimental results demonstrate that the model achieves an average accuracy of 99.79% in classifying six types of road targets, significantly outperforming other methods. This research is of significant importance in advancing the understanding of road traffic conditions by autonomous driving systems and enhancing system safety.

Classification and Recognition of Lung Sounds Using Artificial Intelligence and Machine Learning: A Literature Review

This review explores the latest advances in artificial intelligence (AI) and machine learning (ML) for the identification and classification of lung sounds. The article provides a historical overview from the invention of the electronic stethoscope to the auscultation of lung sounds, emphasizing the importance of the rapid diagnosis of lung diseases in the post-COVID-19 era. The review classifies lung sounds, including wheezes and stridors, and explores their pathological relevance. In addition, the article deeply explores feature extraction strategies, measurement methods, and multiple advanced machine learning models for classification, such as deep residual networks (ResNets), convolutional neural networks combined with long short-term memory networks (CNN–LSTM), and transformer models (transformer). The article discusses the problems of insufficient data and replicating human expert experience and proposes future research directions, including improved data utilization, enhanced feature extraction, and classification using spectrograms. Finally, the article emphasizes the expanding role of AI and ML in lung sound diagnosis and their potential for further development in this field.

A cross-chain-based approach for secure data sharing and interoperability in electronic health records using blockchain technology

In most healthcare facilities, Electronic Health Records (EHRs) have changed paper-based medical records in the healthcare industry. Nevertheless, there are issues with credibility, management, and secure data storage with the existing EHR frameworks. In order to fill this, this work establishes a cross-chain approach for EHR record sharing that is based on the interoperability of EHRs. Different entities, like patients, healthcare providers, Government Agencies (GA), blockchain, and ledger are included in the proposed system. Several processes including initialization, registration process, agreement and verification process, bank fund transfer and certificate generation, health record exchange among two hospitals, and secret key generation, are used to implement the newly developed authentication approach. In addition, the key is obtained from the interior layers of Deep Residual SpinalNet (Deep RSNet), which is utilized to encrypt the EHR records. The Deep RSNet is the integration of Deep Residual Network (DRN) and SpinalNet. The investigation of the developed technique exposes the supremacy of the Deep RSNet model by achieving minimal values of response time, memory usage, and normalized variance of 53.677 sec, 2.682 MB, and 1.346 respectively.

Improving lithofacies prediction in lacustrine shale by combining deep learning and well log curve morphology in Sanzhao Sag, Songliao Basin, China

The accurate identification of shale lithofacies is crucial for characterizing the hydrocarbon potential of lacustrine shale oil reservoirs. Petrophysical logging, serving as an effective tool for acquiring subsurface lithofacies information, provides a convenient and reliable lithofacies identification solution. Deep learning technology, capable of adapting to the nonlinearity and non-stationarity inherent in geological statistics, exhibits unique advantages in conventional reservoir lithofacies prediction. However, the lithofacies of lacustrine shale formations undergo rapid spatial and temporal changes, rendering lithofacies prediction more complex compared to conventional reservoirs. In this study, the lower Qingshankou member in the Sanzhao Sag was selected as the research target, and the Deep Residual Shrinkage Network (DRSN), known for its ability to handle complex nonlinear relationships and mitigate the effects of noisy data through residual connections and shrinkage mechanisms, was employed as a deep learning framework for predicting lithofacies in lacustrine shale formations for the first time. Well logging data, including natural gamma ray (GR), acoustic (AC), deep investigate double lateral resistivity log (RD), shallow investigate double lateral resistivity log (RS), and corrected compensated neutron log (CNC), were used as input features for the model. The results indicate that the DRSN model achieves an accuracy of 76.3% in predicting lithofacies in lacustrine shale formations. However, the DRSN model still exhibits shortcomings in capturing lithofacies change information. To enhance the model's ability to identify lithofacies change interfaces, this study further explicitly introduces Well Logging Curve Morphological Features (WLCM) as additional features and establishes a recognition method combining DRSN with WLCM. The combined DRSN-WLCM model was validated using a separate test dataset, demonstrating an improved accuracy of 85.5%, using the five well logging attributes and the derivative of the AC as inputs. Furthermore, the study reveals the lithofacies spatial distribution characteristics of the lower Qingshankou member in the Sanzhao Sag. This method can be widely applied to lithofacies delineation in lacustrine shale formations and similar stratigraphic units.