Review of intelligent diagnosis methods for imaging gland cancer based on machine learning

Gland cancer is a high-incidence disease that endangers human health,and its early detection and treatment require efficient,accurate,and objective intelligent diagnosis methods.In recent years,the advent of machine learning techniques has yielded satisfactory results in intelligent gland cancer diagnosis based on clinical images,significantly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors.The focus of this study is to review,classify,and analyze intelligent diagnosis methods for imaging gland cancer based on machine learning and deep learning.This paper briefly introduces some basic imaging principles of multimodal medical images,such as the commonly used computed tomography(CT),magnetic resonance imaging(MRI),ultrasound(US),positron emission tomography(PET),and pathology.In addition,the intelligent diagnosis methods for imaging gland cancer were further classified into supervised learning and weakly supervised learning.Supervised learning consists of traditional machine learning methods,such as K-nearest neighbor algorithm(KNN),support vector machine(SVM),and multilayer perceptron,and deep learning methods evolving from convolutional neural network(CNN).By contrast,weakly supervised learning can be further categorized into active learning,semisupervised learning,and transfer learning.State-of-the-art methods are illustrated with implementation details,including image segmentation,feature extraction,and optimization of classifiers.Their performances are evaluated through indicators,such as accuracy,precision,and sensitivity.In conclusion,the challenges and development trends of intelligent diagnosis methods for imaging gland cancer were addressed and discussed.

Intelligent diagnosis of northern corn leaf blight with deep learning model

Maize(Zea mays L.), also known as corn, is the third most cultivated crop in the world. Northern corn leaf blight(NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica(Luttrell) Leonard and Suggs. Early intelligent diagnosis and warning is an effective and economical strategy to control this disease. Today, deep learning is beginning to play an essential role in agriculture. Notably, deep convolutional neural networks(DCNN) are amongst the most successful machine learning techniques in plant disease detection and diagnosis. Our study aims to identify NCLB in the maize-producing area in Jilin Province based on several DCNN models. We established a database of 985 leaf images of healthy and infected maize and applied data augmentation techniques including image segmentation, image resizing, image cropping, and image transformation, to expand to 30 655 images. Several proven convolutional neural networks, such as AlexNet, Google Net, VGG16, and VGG19, were then used to identify diseases. Based on the best performance of the DCNN pre-trained model Google Net, some of the recent loss functions developed for deep facial recognition tasks such as Arc Face, Cos Face, and A-Softmax were applied to detect NCLB. We found that a pre-trained Google Net architecture with the Softmax loss function can achieve an excellent accuracy of 99.94% on NCLB diagnosis. The analysis was implemented in Python with two deep learning frameworks, Pytorch and Keras. The techniques, training, validation, and test results are presented in this paper. Overall, our study explores intelligent identification technology for NCLB and effectively diagnoses NCLB from images of maize.

Method of Mechanical Fault Intelligent Diagnosis Based on Vibration Signal of High Voltage Circuit Breaker

Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(SVM)to construct classifier for fault diagnosis is presented.The acceleration sensors are applied to collecting the vibration data of different states of high voltage circuit breakers based on self-made experimental platform in this method.The wavelet packet are fully applied to analyze the vibration signal and decompose vibration signal into three layers,and wavelet packet energy entropy of each frequency band are as the characteristic vector of circuit breaker failure mode.Then the intelligent diagnosis network is established on the basis of the support vector machine theory.It is verified that the method has a better capability of classification and a higher accuracy compared with the traditional neural network diagnosis method through distinguishing the three fault modes which are tripping device stuck,the vacuum arcing chamber fixed bolt looseness and too much friction force of the transmission mechanism of circuit breaker in this paper.

Design and Application of Intelligent Diagnosis System for three Pumping Stations of Stamping Automation

This paper focuses on the maintenance of automotive stamping automation equipment.Through long-term self-study and accumulated experience,we independently developed a process monitoring system based on the three pumping stations of clutch,tension pad and lubrication in the stamping automation production line,which is used for real-time monitoring and diagnosis in the automatic production process without stopping the machine,and for the detection of oil temperature change,high-pressure pipeline leakage and oil return pipe In this paper,the improved case has strong practicability,low development cost,and has been recognized by peers in terms of cost efficiency improvement,which is easy to be popularized.

Intelligent Diagnosis of Short Hydraulic Signal Based on Improved EEMD and SVM with Few Low-dimensional Training Samples

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD（empirical mode decomposition） and EEMD（ensemble empirical mode decomposition） are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM（support vector machine）, extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD＋SVM method is compared with the EEMD＋SVM and EMD＋SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

Review of research on intelligent diagnosis of oil transfer pump malfunction

Oil transfer pump is the key dynamic equipment in the process of oil and gas gathering and transportation,and its working reliability directly affects the safety of oil and gas storage and transportation.Intelligent diagnosis is a key technical method to reduce failure rate of oil transfer pump,ensure the safety of gathering and transportation process,and avoid major safety accidents caused by oil transfer pump failure.Various oil transfer pumps have been emerged in recent decades,and the common fault types and characteristics of oil transfer pump have been brought out in the review.This article highlights on the research of the fault signal and processing methods of oil transfer pump.Firstly,the fault signal of the oil transfer pump is discussed and the advantages and disadvantages of different signal extraction are analyzed.Secondly,the intelligent diagnosis method of oil transfer pump and the shortcomings of the existing methods are pointed out.Finally,the conclusions are given and the future development perspectives of oil transfer pumps are suggested.The main contribution of this review is to give a syn-thetic understanding on oil transfer pumps.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Artificial intelligence assisted pterygium diagnosis:current status and perspectives

Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potential in assisting clinicians with pterygium diagnosis.This paper provides an overview of AI-assisted pterygium diagnosis,including the AI techniques used such as machine learning,deep learning,and computer vision.Furthermore,recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection,classification and segmentation were summarized.The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed.The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis,which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development

Disorders of sex development(DSD)are a group of rare complex clinical syndromes with multiple etiologies.Distinguishing the various causes of DSD is quite difficult in clinical practice,even for senior general physicians because of the similar and atypical clinical manifestations of these conditions.In addition,DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD.Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses.On the basis of the principles and algorithms of dynamic uncertain causality graph(DUCG),a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence.“Chaining”inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information.Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis.The model had an accuracy of 94.1%,which was significantly higher than that of interns and third-year residents.In conclusion,the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSDrelated diseases.

Deep Learning-Based Stacked Auto-Encoder with Dynamic Differential Annealed Optimization for Skin Lesion Diagnosis

Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.

The Research on Hybrid Intelligent Fault-diagnosisSystem of CNC Machine Tools

After analyzing the structure and characteristics of the hybrid intelligent diagnosis system of CNC machine toolsCNC-HIDS), we describe the intelligent hybrid mechanism of the CNC-HIDS, and present the evaluation and the running instance of the system. Through tryout and validation, we attain satisfactory results.

Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

The performance of traditional vibration based fault diagnosis methods greatly depends on those hand- crafted features extracted using signal processing algo- rithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised represen- tation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal struc- tures, this paper proposes a multiscale representation learning （MSRL） framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at dif- ferent scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multi- scale representations. Finally, the multiscale representa- tions are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies： motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

Intelligent fault diagnosis methods toward gas turbine: A review

Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling engine systems or certain components implement faults detection and diagnosis based on the measurement of systemic parameters deviations. However, these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature, measurement uncertainty, fault coupling and other implementing problems. Recently, the development of artificial intelligence algorithms has provided an effective solution to the above problems, triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance, and universality. This paper presents a systematic review of recently proposed intelligent fault diagnosis methods for GT engines, according to the classification of shallow learning methods, deep learning methods and hybrid intelligent methods. Moreover, the principle of typical algorithms, the evolution of enhanced methods, and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis. Possible directions for development in method validation, information fusion, and interpretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.

Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic data.When applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be addressed.Firstly,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured data.Secondly,privacy of patients’data needs to be protected,as these data contain the most sensitive and private information.Lastly,considering the practicality of the model,the computational requirements should not be too high.To address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage diseases.This method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an arbiter.It also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in modeling.Finally,this paper designs and conducts experiments to evaluate the proposed solution.The experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

Algorithm of automatic identification of diabetic retinopathy foci based on ultra-widefield scanning laser ophthalmoscopy

AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN features)+ResNet50(Residua Network 50)+FPN(Feature Pyramid Networks)method for detecting hemorrhagic spots,cotton wool spots,exudates,and microaneurysms in DR ultra-widefield SLO.Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate.Feature fusion was carried out by the feature pyramid network FPN,which significantly improved lesion detection rates in SLO fundus images.RESULTS:By analyzing 1076 ultra-widefield SLO images provided by our hospital,with a resolution of 2600×2048 dpi,the accuracy rates for hemorrhagic spots,cotton wool spots,exudates,and microaneurysms were found to be 87.23%,83.57%,86.75%,and 54.94%,respectively.CONCLUSION:The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO,providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.

Mechanical Fault Diagnosis Using Support Vector Machine

The Support Vector Machine (SVM) is a machine learning algorithm based on theStatistical Learning Theory (SLT), which can get good classification effects even with a fewlearning samples. SVM represents a new approach to pattern classification and has been shown to beparticularly successful in many fields such as image identification and face recognition. It alsoprovides us with a new method to develop intelligent fault diagnosis. This paper presents aSVM-based approach for fault diagnosis of rolling bearings. Experimentation with vibration signalsof bearings is conducted. The vibration signals acquired from the bearings are used directly in thecalculating without the preprocessing of extracting its features. Compared with the methods basedon Artificial Neural Network (ANN), the SVM-based method has desirable advantages. It is applicablefor on-line diagnosis of mechanical systems.

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.

Keynote Summaries of the First International Symposium on Dynamics,Monitoring,and Diagnostics

The first International Symposium on Dynamics,Monitoring,and Diagnostics was held in Chongqing,China,in April 2022.The Symposium,which was attended both virtually and in person,had an audience of 2000 and was aimed at enhancing the intelligence of condition monitoring for engineering systems.During the Symposium,five keynote addresses were delivered by world leading experts,and this paper is comprised of summaries of these addresses to ensure that the important messages of these speakers are properly on record and readily able to be referenced.

Development of Intelligent Acupuncture Applications and Related Technologies

The study focuses on developing mobile applications based on intelligent acupuncture, classified into education and intelligent diagnosis and treatment. The mobile application function is divided into two directions: assisting acupoint positioning and perfecting acupoint knowledge system. The study does a relative review on Android and IOS, showing that the number of Android users is rather more prevailing than IOS. It suggests that intelligent acupuncture mobile applications should comply with the trend of acupuncture globalization in the future, developing multilingual versions. Further, the content should be characterized by the experience of different famous doctors to avoid homogenization. Technically, intelligent applications should continue to develop three-dimensional and augmented reality technology, to optimize the accuracy of acupoint positioning.

Intrinsic component filtering for fault diagnosis of rotating machinery

Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.