Diagnosis Model Research Articles

Data Privacy Preserving for Centralized Robotic Fault Diagnosis With Modified Dataset Distillation

Abstract Industrial robots generate monitoring data rich in sensitive information, often making enterprises reluctant to share, which impedes the use of data in fault diagnosis modeling. Dataset distillation (DD) is an effective approach to condense large dataset into smaller, synthesized forms, focusing solely on fault-related features, which facilitates secure and efficient data transfer for diagnostic purposes. However, the challenge of achieving satisfactory fault diagnosis accuracy with distilled data stems from the computational complexity in data distillation process. To address this problem, this article proposes a modified KernelWarehouse (MKW) network-based DD method to achieve accurate fault diagnosis with the distilled dataset. In this algorithm, DD first generates distilled training and testing dataset, followed by the training of an MKW-based network based on these distilled datasets. Specifically, MKW reduces network complexity through the division of static kernels into disjoint kernel cells, which are then computed as linear mixtures from a shared warehouse. An experimental study based on the real-world robotic dataset reveals the effectiveness of the proposed approach. The experimental results indicate that the proposed method can achieve a fault diagnosis accuracy of 86.3% when only trained with distilled data.

A General and Efficient Fault Diagnosis Approach Using Hyperdimensional Computing Enhanced by Local Feature Extraction

Abstract Ensuring the safe and reliable operation of industrial equipment is of great importance, and accordingly, machine fault diagnosis has attracted considerable attention. Recently, various deep learning-based fault diagnosis models have been widely developed due to the powerful ability of feature learning and nonlinear modeling of deep learning models, and these methods heavily rely on large amounts of data and high memory and resources. However, it is difficult to obtain enough data in practical applications, thus leading to the unsatisfactory performance of deep learning-based models and high cost. In this paper, we propose a new and efficient fault diagnosis approach based on Hyperdimensional computing (HDC), which can learn high-dimensional, random, and holographic distributed representations of input features. It has the advantages of single training, low training cost, and does not require a large amount of data. Therefore, we apply HDC to the field of few-shot fault diagnosis for the first time, and propose a simple and efficient approach for few-shot fault diagnosis based on hyperdimensional computing with local feature extraction(L-HDC). Instead of using original data, the model firstly extracts the local features of original data by hand-designed features, and then carries out HDC-encoding, training, and test classification. Through the comparison of various models and the analysis of various data, the effectiveness of our proposed model is verified. In the case of few-shot learning, the recognition accuracy is as high as 90\% and the training time of ordinary HDC model is 5-10 times that of L-HDC model, which is superior to other models.

A minimalistic approach to classifying Alzheimer’s disease using simple and extremely small convolutional neural networks

Background:There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer’s disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imaging (MRI). The goal of the current study is to investigate whether modern, flexible architectures such as EfficientNet provide any performance boost over more standard architectures. Methods:MRI data was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and processed with a minimal preprocessing pipeline. Among the various architectures tested, the minimal 3D convolutional neural network SFCN stood out, composed solely of 3x3x3 convolution, batch normalization, ReLU, and max-pooling. We also examined the influence of scale on performance, testing SFCN versions with trainable parameters ranging from 720 up to 2.9 million. Results:SFCN achieves a test ROC AUC of 96.0% while EfficientNet got an ROC AUC of 94.9 %. SFCN retained high performance down to 720 trainable parameters, achieving an ROC AUC of 91.4%. Comparison with existing methods:The SFCN is compared to DenseNet and EfficientNet as well as the results of other publications in the field. Conclusions:The results indicate that using the minimal 3D convolutional neural network SFCN with a minimal preprocessing pipeline can achieve competitive performance in AD classification, challenging the necessity of employing more complex architectures with a larger number of parameters. This finding supports the efficiency of simpler deep learning models for neuroimaging-based AD diagnosis, potentially aiding in better understanding and diagnosing Alzheimer’s disease.

Clinical and dermoscopy image-based deep learning models for skin lesion diagnosis in clinical practice

Clinical and dermoscopy image-based deep learning models for skin lesion diagnosis in clinical practice

Machine learning model for non-alcoholic steatohepatitis diagnosis based on ultrasound radiomics

BackgroundNon-Alcoholic Steatohepatitis (NASH) is a crucial stage in the progression of Non-Alcoholic Fatty Liver Disease(NAFLD). The purpose of this study is to explore the clinical value of ultrasound features and radiological analysis in predicting the diagnosis of Non-Alcoholic Steatohepatitis.MethodAn SD rat model of hepatic steatosis was established through a high-fat diet and subcutaneous injection of CCl4. Liver ultrasound images and elastography were acquired, along with serum data and histopathological results of rat livers.The Pyradiomics software was used to extract radiomic features from 2D ultrasound images of rat livers. The rats were then randomly divided into a training set and a validation set, and feature selection was performed through dimensionality reduction. Various machine learning (ML) algorithms were employed to build clinical diagnostic models, radiomic models, and combined diagnostic models. The efficiency of each diagnostic model for diagnosing NASH was evaluated using Receiver Operating Characteristic (ROC) curves, Clinical Decision Curve Analysis (DCA), and calibration curves.ResultsIn the machine learning radiomic model for predicting the diagnosis of NASH, the Area Under the Curve (AUC) of ROC curve for the clinical radiomic model in the training set and validation set were 0.989 and 0.885, respectively. The Decision Curve Analysis revealed that the clinical radiomic model had the highest net benefit within the probability threshold range of > 65%. The calibration curve in the validation set demonstrated that the clinical combined radiomic model is the optimal method for diagnosing Non-Alcoholic Steatohepatitis.ConclusionThe combined diagnostic model constructed using machine learning algorithms based on ultrasound image radiomics has a high clinical predictive performance in diagnosing Non-Alcoholic Steatohepatitis.

An approach for fault-related monitoring variables selection based on dual-layer correlation networks

PurposeFault-related monitoring variables selection is a process of obtaining a subset of variables from the original set, which is of great significance for reducing information redundancy and improving the performance of the fault diagnosis models. This paper aims to propose a novel variables selection approach based on complex networks.Design/methodology/approachFirstly, a dual-layer correlation networks (DlCN) which consists of mechanism-oriented correlation sub-network (MoCSN) and data-oriented correlation sub-network (DoCSN) is constructed. Secondly, an algorithm for identifying critical fault-related monitoring variables based on dual correlations is introduced. In the algorithm, the topological attributes of the MoCSN and correlation threshold of the DoCSN are used successively.FindingsIn the experiments of vertical elevator fault diagnosis, the critical fault-related monitoring variables selected by the DlCN-based approach is more effective than the traditional approaches. It indicates that fusion mechanism-oriented correlation can enhance the comprehensiveness of variable correlation analysis. Moreover, the approach has been proved to be adaptable to different fault diagnosis models.Originality/valueIn the DlCN-based variables selection approach, the mechanism-oriented correlation and data-oriented correlation are comprehensively considered. It improves the precision of variables selection. Meanwhile, it is an unsupervised and model-agnostic approach which addresses the shortcomings of some conventional approaches that require data labels and have insufficient adaptability for fault diagnosis models.

Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans

To evaluate whether radiomics models based on unenhanced paranasal sinuses CT images could be a useful tool for differentiating inverted papilloma (IP) from chronic rhinosinusitis with polyps (CRSwNP). This retrospective study recruited 240 patients with CRSwNP and 106 patients with IP from three centers. 253 patients from Qilu Hospital were randomly divided into the training set (n = 151) and the internal validation set (n = 102) with a ratio of 6:4. 93 patients from the other two centers were used as the external validation set. The patients with the unilateral disease (n = 115) from Qilu Hospital were selected to further develop a subgroup analysis. Lesion segmentation was manually delineated in CT images. Least absolute shrinkage and selection operator algorithm was performed for feature reduction and selection. Decision tree, support vector machine, random forest, and adaptive boosting regressor were employed to establish the differential diagnosis models. 43 radiomic features were selected for modeling. Among the models, RF achieved the best results, with an AUC of 0.998, 0.943, and 0.934 in the training set, the internal validation set, and the external validation set, respectively. In the subgroup analysis, RF achieved an AUC of 0.999 in the training set and 0.963 in the internal validation set. The proposed radiomics models offered a non-invasion and accurate differential approach between IP and CRSwNP and has some significance in guiding clinicians determining the best treatment plans, as well as predicting the prognosis.

Stack performance classification and fault diagnosis optimization of solid oxide fuel cell system based on bayesian artificial neural network and feature selection

Solid oxide fuel cell (SOFC) has attracted much attention because of its high efficiency and silent power generation, and can be applied to the field of fuel cell-powered unmanned boats. However, a fuel deficit failure in the system can lead to reduced stack performance, reducing the reliability of SOFC applications in the field of unmanned boats. Therefore, this paper proposes an efficient breakdown diagnosis model for SOFC system fault diagnosis. Firstly, Bayesian artificial neural network is constructed by using the best feature data subset after Relief-F combined with minimum Redundancy Maximum Relevance algorithm feature selecting. Secondly, the correct diagnosis rate under different number of optimal features is calculated, and the best feature combinations are combined to verify. Finally, the particle swarm algorithm selects the optimal hidden neuron count to optimization model. Experimental findings suggest that for correct diagnosis rate, the breakdown diagnosis model based on the optimal number of 3 features can reduce the training time and maintain high accuracy compared with the use of all 34 features. This research result provides a feasible way for the fault diagnosis application of SOFC system power generation in the field of unmanned boats.

Multiple Instance Pathology Image Diagnosis Model based on Channel Attention and Data Augmentation

The application of machine learning in the medical field has resulted in significant advancements in computer-aided pathological diagnosis. Multiple instance learning (MIL) has emerged as a promising approach for pathological image classification, particularly in scenarios where local annotations are lacking. However, current MIL models often overlook the importance of feature weights in the channel dimension and struggle with imbalanced positive and negative data. To address these limitations, an integration of a channel attention (CA) module and an augmented data (AUG) mechanism into the MIL model is proposed, resulting in improved performance. The CA module dynamically assigns weights to example features in the channel dimension, enhancing or suppressing features adaptively. Additionally, the AUG mechanism effectively balances the distribution of positive and negative data, significantly reducing false negatives. Through ablation experiments, the contributions of the CA module and AUG mechanism in enhancing the overall model performance are analyzed. Experimental validations on the CAMELYON16/17 public pathological image datasets demonstrate that the proposed model and method outperform existing approaches, with particular emphasis on reducing false negatives.

Adaptive fault diagnosis for high-purity carbonate process based on unsupervised and transfer learning

Reactive distillation integrates reaction and distillation to achieve process intensification. And the increasing process complexity urgently requires fault diagnosis system to ensure its safe and efficient operation. However, establishing fault diagnosis model is facing intractable challenges, e.g. scarce labeled data, operating mode changeover due to product proportion variation. To tackle absent labeled data problem, pseudo-labeled database is established by integrating data mining, density-based spatial clustering of applications with noise algorithm and process knowledge, based on which unsupervised deep learning is developed. Besides, to make intelligent fault diagnosis system adaptive for multimode operation changeover, transfer learning with domain adaptation strategy is adopted, which could mitigate different data distributions in various operating states and transfer knowledge learned from source domain to target domain. Taking carbonate ester process with reactive distillation as benchmark, the performance and superiority of unsupervised learning and transfer learning is verified and demonstrated in solving the corresponding puzzle.

Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography

BackgroundWe aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography.MethodsPanoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1.ResultsTo evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43–94.26%, 52.63–60.77%, 72.01–75.84%, and 76.36–79.00%, respectively.ConclusionWe found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.

A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images

A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images

Dried Blood Spot Metabolome Features of Ischemic-Hypoxic Encephalopathy: A Neonatal Rat Model.

Hypoxic-ischemic encephalopathy (HIE) is a severe neurological disorder caused by perinatal asphyxia with significant consequences. Early recognition and intervention are crucial, with therapeutic hypothermia (TH) being the primary treatment, but its efficacy depends on early initiation of treatment. Accurately assessing the HIE severity in neonatal care poses challenges, but omics approaches have made significant contribution to understanding its complex pathophysiology. Our study further explores the impact of HIE on the blood metabolome over time and investigated changes associated with hypothermia's therapeutic effects. Using a rat model of hypoxic-ischemic brain injury, we comprehensively analyzed dried blood spot samples for fat-soluble compounds using HPLC-MS. Our research shows significant changes in the blood metabolome after HIE, with a particularly rapid recovery of lipid metabolism observed. Significant changes in lipid metabolites were observed after 3 h of HIE, including increases in ceramides, carnitines, certain fatty acids, phosphocholines, and phosphoethanolamines, while sphingomyelins and N-acylethanolamines (NAEs) decreased (p < 0.05). Furthermore, NAEs were found to be significant features in the OPLS-DA model for HIE diagnosis, with an area under the curve of 0.812. TH showed a notable association with decreased concentrations of ceramides. Enrichment analysis further corroborated these observations, showing modulation in several key metabolic pathways, including arachidonic acid oxylipin metabolism, eicosanoid metabolism via lipooxygenases, and leukotriene C4 synthesis deficiency. Our study reveals dynamic changes in the blood metabolome after HIE and the therapeutic effects of hypothermia, which improves our understanding of the pathophysiology of HIE and could lead to the development of new rapid diagnostic approaches for neonatal HIE.

A radiomics nomogram based on multiparametric MRI for diagnosing focal cortical dysplasia and initially identifying laterality

BackgroundFocal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation. The diagnosis of FCD is challenging. We generated a radiomics nomogram based on multiparametric magnetic resonance imaging (MRI) to diagnose FCD and identify laterality early.MethodsForty-three patients treated between July 2017 and May 2022 with histopathologically confirmed FCD were retrospectively enrolled. The contralateral unaffected hemispheres were included as the control group. Therefore, 86 ROIs were finally included. Using January 2021 as the time cutoff, those admitted after January 2021 were included in the hold-out set (n = 20). The remaining patients were separated randomly (8:2 ratio) into training (n = 55) and validation (n = 11) sets. All preoperative and postoperative MR images, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and combined (T1w + T2w + FLAIR) images, were included. The least absolute shrinkage and selection operator (LASSO) was used to select features. Multivariable logistic regression analysis was used to develop the diagnosis model. The performance of the radiomic nomogram was evaluated with an area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration and clinical utility.ResultsThe model-based radiomics features that were selected from combined sequences (T1w + T2w + FLAIR) had the highest performances in all models and showed better diagnostic performance than inexperienced radiologists in the training (AUCs: 0.847 VS. 0.664, p = 0.008), validation (AUC: 0.857 VS. 0.521, p = 0.155), and hold-out sets (AUCs: 0.828 VS. 0.571, p = 0.080). The positive values of NRI (0.402, 0.607, 0.424) and IDI (0.158, 0.264, 0.264) in the three sets indicated that the diagnostic performance of Model-Combined improved significantly. The radiomics nomogram fit well in calibration curves (p > 0.05), and decision curve analysis further confirmed the clinical usefulness of the nomogram. Additionally, the contrast (the radiomics feature) of the FCD lesions not only played a crucial role in the classifier but also had a significant correlation (r = -0.319, p < 0.05) with the duration of FCD.ConclusionThe radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.

Detection and Segmentation of Mouth Region in Stereo Stream Using YOLOv6 and DeepLab v3+ Models for Computer-Aided Speech Diagnosis in Children

This paper describes a multistage framework for face image analysis in computer-aided speech diagnosis and therapy. Multimodal data processing frameworks have become a significant factor in supporting speech disorders’ treatment. Synchronous and asynchronous remote speech therapy approaches can use audio and video analysis of articulation to deliver robust indicators of disordered speech. Accurate segmentation of articulators in video frames is a vital step in this agenda. We use a dedicated data acquisition system to capture the stereovision stream during speech therapy examination in children. Our goal is to detect and accurately segment four objects in the mouth area (lips, teeth, tongue, and whole mouth) during relaxed speech and speech therapy exercises. Our database contains 17,913 frames from 76 preschool children. We apply a sequence of procedures employing artificial intelligence. For detection, we train the YOLOv6 (you only look once) model to catch each of the three objects under consideration. Then, we prepare the DeepLab v3+ segmentation model in a semi-supervised training mode. As preparation of reliable expert annotations is exhausting in video labeling, we first train the network using weak labels produced by initial segmentation based on the distance-regularized level set evolution over fuzzified images. Next, we fine-tune the model using a portion of manual ground-truth delineations. Each stage is thoroughly assessed using the independent test subset. The lips are detected almost perfectly (average precision and F1 score of 0.999), whereas the segmentation Dice index exceeds 0.83 in each articulator, with a top result of 0.95 in the whole mouth.

Application of Machine Learning in the field of Heart Disease Prediction and its Accuracy Study

The application of machine learning in the healthcare sector is a prominent area of research presently. Among these, there is significant potential in employing machine learning for predicting and treating heart disease. Hence, this study aims to explore the accuracy and application of machine learning models in predictive diagnosis and treatment of heart disease. This paper first presents commonly used machine learning algorithms within relevant fields, followed by gathering data and instances showcasing the use of machine learning in predicting and treating heart disease. Through data analysis, this study summarizes current cutting-edge development directions, trends, as well as the integration of machine learning algorithms into real medical processes. Finally, it concludes by summarizing the existing prospects and challenges faced by machine learning in predicting and treating heart disease. This paper can serve as a valuable source of inspiration and reference for researchers involved in related fields concerning heart disease prediction and treatment.

MLC-DKT: A multi-layer context-aware deep knowledge tracing model

As a fundamental research task for intelligent education, knowledge tracing (KT) aims to trace learners’ dynamic knowledge states and predict their future performance based on their historical response data. However, learners’ cognitive processes cannot be studied in isolation from the context; nevertheless, existing knowledge tracing models mainly use knowledge topology as auxiliary information but do not deeply explore the contextual information of exercises. Thus, an in-depth exploration of multi-layer contextual information in learning scenarios is required, focusing on the cognitive differences among different learners. Furthermore, knowledge tracing models exhibit limited interpretability compared with cognition diagnosis methods integrating educational priors. To address these issues, we propose a multi-layer context-aware deep knowledge tracing (MLC-DKT) model. Specifically, we first present the multi-layer contextual representation method involving knowledge concepts and exercises. In the knowledge state tracing module, we incorporate the learners’ knowledge priors into the attention mechanism to capture the evolution of the learners’ knowledge. Then, we develop a calculation method for the similarity effect among the contextual information, which helps estimate the learners’ knowledge states. In the learning performance prediction module, we introduce the guessing and slipping factors from the cognition diagnosis model to optimize the MLC-DKT model, further improving the performance of the KT model. Finally, we conduct extensive experiments on real-world datasets, demonstrating that the MLC-DKT model provides improved predictions of learner performance. Moreover, the MLC-DKT model realizes contextual awareness from knowledge concepts and exercises, making the predictions more interpretable.

A Hybrid Lung Cancer Model for Diagnosis and Stage Classification from Computed Tomography Images

Pulmonary cancers at early stages is difficult but crucial for patient survival. Therefore, it is essential to develop an intelligent, autonomous, and accurate lung cancer detection system that shows great reliability compared to previous systems and research. In this study, we have developed an innovative lung cancer detection system known as the Hybrid Lung Cancer Stage Classifier and Diagnosis Model (Hybrid-LCSCDM). This system simplifies the complex task of diagnosing lung cancer by categorizing patients into three classes: normal, benign, and malignant, by analyzing computed tomography (CT) scans using a two-part approach: First, feature extraction is conducted using a pre-trained model called VGG-16 for detecting key features in lung CT scans indicative of cancer. Second, these features are then classified using a machine learning technique called XGBoost, which sorts the scans into three categories. A dataset, IQ-OTH/NCCD - Lung Cancer, is used to train and evaluate the proposed model to show its effectiveness. The dataset consists of the three aforementioned classes containing 1190 images. Our suggested strategy achieved an overall accuracy of 98.54 %, while the classification precision among the three classes was 98.63 %. Considering the accuracy, recall, and precision as well as the F1-score evaluation metrics, the results indicated that when using solely computed tomography scans, the proposed (Hybrid-LCSCDM) model outperforms all previously published models.

Fault diagnosis of complex industrial equipment based on chunked statistical global-local feature fusion

Abstract Industrial process equipment is bulky and complex in structure, which is easy to produce faults during operation and affect production efficiency, cause huge economic losses, and even threaten the safety of workers. To achieve sustainable operation of large-scale industrial processes, timely and accurate monitoring and handling of abnormal situations are essential. However, fault monitoring of large equipment requires the collection of abundant data, which includes many complex related variables, resulting in excessive redundant data generated during the fault monitoring process. Moreover, the existing principal component analysis (PCA) method can only retain the global characteristic of variance information, and cannot obtain the local characteristic that can characterize the topological relationship between the data points, which affects its monitoring reliability and intelligence level. In response to these issues, a fault diagnosis model for complex industrial processes based on chunked statistical global-local feature fusion (CSGLFF) is proposed in this paper. First, considering the correlation characteristics between industrial process variables, a correlation variable chunking method mutual information-based is designed to merge the variables with small correlation to obtain the optimal chunking of variables. Second, PCA and locality preserving projections (LPP) are combined to construct a global-local feature fusion (GLFF) model that can extract global and local features simultaneously. The chunked data are imported into the GLFF for the extraction of its features respectively, and the corresponding CSGLFF is established. In addition, Bayesian inference is used to fuse the statistics of each sub-chunk to establish an overall fault monitoring statistical indicators, and the reason for failure is found through the variable contribution graph. Finally, two cases of Tennessee Eastman process (TEP) and laboratory emulsion pump were used to conduct experimental research on the performance of CSGLFF. The results show that compared with the chunked statistical PCA, chunked statistical LPP, and GLFF algorithms, The accuracy of fault monitoring for TEP mean, flow pulsation impact, and pressure anomaly of this method reached 92.91%, 97%, and 90.30%, respectively. It has good monitoring effect in processing data with large variables, reducing the generation of redundant data, improving the accuracy of industrial monitoring, and accurately identifying the relevant variables of fault occurrence. This provides a theoretical basis for determining the fault location and points out the direction for maintenance by staff.

Rolling bearing fault diagnosis method based on multi-scale pooling residual convolutional neural network under noisy environment

To address the issues of unstable performance and poor generalization ability of bearing fault diagnosis model caused by strong noise and variable operating conditions in actual production，this paper proposes a rolling bearing fault diagnosis method based on MSPRCNN model. By converting vibration signals to frequency domain with FTand utilizing wide convolution kernels for feature extraction, the approach aims to enhance fault detection. A MSPFE module captures information at different scales to simplify complexity, while an UPA module establishes correlations between frequency domain positions. To reduce the impact of noise and address the vanishing gradient problem, the MSPRCNN model employs GC instead of standard convolution, and utilizes the IReLU activation function to improve model feature representation. Experimental results on two datasets show that the fault recognition accuracy is 98.71% under variable loads and 98.2% under variable speeds. The MSPRCNN model outperforms other methods in fault recognition and generalization in noisy environments.