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Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 24025

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Prof. Dr. José Rodellar

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Department of Mathematics, Barcelona East School of Engineering (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal Besòs, Building A, Av. Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: structural vibration control; damage identification; large scale control; decentralized control; control of irrigation canals; automatic image analysis of blood cells
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Dr. Francesc Pozo

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Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
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Dr. Yolanda Vidal

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Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: condition monitoring; data-based models; fault diagnosis; fault tolerant control; machine learning; structural health monitoring; sensors; wind turbines
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Special Issue Information

Dear Colleagues,

Machine and deep learning algorithms have recently seen broad use in computer assisted diagnostic systems due to their dramatic advances in image analysis, computer vision, and time-series analysis. Deep and machine learning have demonstrated their huge potential to transform computer-aided diagnosis in a wide variety of areas that range from medical disease diagnostics and classification, through mechanical systems condition monitoring, to diagnosis for chemical industries, as well as structural health diagnosis of different structures as bridges, wind turbines, or buildings.

This Special Issue calls for innovative work that explores recent advances, prospects, and challenges in artificial intelligence applications to reduce the chances of either missing, misclassifying, or overdiagnosing suspicious targets on diagnostic, as well as to propel the path into computer assisted prognostics. It is noteworthy that in this Special Issue the keyword ‘diagnostic’ has to be understood in a wide sense: medical, mechanical systems, civil engineering, chemical processes, and so on. The targeted audience includes both academic researchers and industrial practitioners. The purpose is to provide a platform to enhance interdisciplinary research and collaborations, and to share the most innovative ideas in various related fields.

Prof. Dr. José Rodellar
Dr. Francesc Pozo
Dr. Yolanda Vidal
Guest Editors

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Keywords

Deep learning
Machine learning
Artificial intelligence
Fault diagnosis
Damage diagnosis
Disease diagnosis
Medical decision making
Real-time diagnostics

Published Papers (9 papers)

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Research

29 pages, 5361 KiB

Open AccessArticle

MS-CheXNet: An Explainable and Lightweight Multi-Scale Dilated Network with Depthwise Separable Convolution for Prediction of Pulmonary Abnormalities in Chest Radiographs

by Shashank Shetty, Ananthanarayana V S. and Ajit Mahale

Mathematics 2022, 10(19), 3646; https://doi.org/10.3390/math10193646 - 05 Oct 2022

Cited by 7 | Viewed by 1873

Abstract

Pulmonary diseases are life-threatening diseases commonly observed worldwide, and timely diagnosis of these diseases is essential. Meanwhile, increased use of Convolution Neural Networks has promoted the advancement of computer-assisted clinical recommendation systems for diagnosing diseases using chest radiographs. The texture and shape of the tissues in the diagnostic images are essential aspects of prognosis. Therefore, in the latest studies, the vast set of images with a larger resolution is paired with deep learning techniques to enhance the performance of the disease diagnosis in chest radiographs. Moreover, pulmonary diseases have irregular and different sizes; therefore, several studies sought to add new components to existing deep learning techniques for acquiring multi-scale imaging features from diagnostic chest X-rays. However, most of the attempts do not consider the computation overhead and lose the spatial details in an effort to capture the larger receptive field for obtaining the discriminative features from high-resolution chest X-rays. In this paper, we propose an explainable and lightweight Multi-Scale Chest X-ray Network (MS-CheXNet) to predict abnormal diseases from the diagnostic chest X-rays. The MS-CheXNet consists of four following main subnetworks: (1) Multi-Scale Dilation Layer (MSDL), which includes multiple and stacked dilation convolution channels that consider the larger receptive field and captures the variable sizes of pulmonary diseases by obtaining more discriminative spatial features from the input chest X-rays; (2) Depthwise Separable Convolution Neural Network (DS-CNN) is used to learn imaging features by adjusting lesser parameters compared to the conventional CNN, making the overall network lightweight and computationally inexpensive, making it suitable for mobile vision tasks; (3) a fully connected Deep Neural Network module is used for predicting abnormalities from the chest X-rays; and (4) Gradient-weighted Class Activation Mapping (Grad-CAM) technique is employed to check the decision models’ transparency and understand their ability to arrive at a decision by visualizing the discriminative image regions and localizing the chest diseases. The proposed work is compared with existing disease prediction models on chest X-rays and state-of-the-art deep learning strategies to assess the effectiveness of the proposed model. The proposed model is tested with a publicly available Open-I Dataset and data collected from a private hospital. After the comprehensive assessment, it is observed that the performance of the designed approach showcased a 7% to 18% increase in accuracy compared to the existing method. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems)

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21 pages, 3929 KiB

Open AccessArticle

Detection of Abnormal Cardiac Response Patterns in Cardiac Tissue Using Deep Learning

by Xavier Marimon, Sara Traserra, Marcel Jiménez, Andrés Ospina and Raúl Benítez

Mathematics 2022, 10(15), 2786; https://doi.org/10.3390/math10152786 - 05 Aug 2022

Cited by 3 | Viewed by 1661

Abstract

This study reports a method for the detection of mechanical signaling anomalies in cardiac tissue through the use of deep learning and the design of two anomaly detectors. In contrast to anomaly classifiers, anomaly detectors allow accurate identification of the time position of the anomaly. The first detector used a recurrent neural network (RNN) of long short-term memory (LSTM) type, while the second used an autoencoder. Mechanical contraction data present several challanges, including high presence of noise due to the biological variability in the contraction response, noise introduced by the data acquisition chain and a wide variety of anomalies. Therefore, we present a robust deep-learning-based anomaly detection framework that addresses these main issues, which are difficult to address with standard unsupervised learning techniques. For the time series recording, an experimental model was designed in which signals of cardiac mechanical contraction (right and left atria) of a CD-1 mouse could be acquired in an automatic organ bath, reproducing the physiological conditions. In order to train the anomaly detection models and validate their performance, a database of synthetic signals was designed (n = 800 signals), including a wide range of anomalous events observed in the experimental recordings. The detector based on the LSTM neural network was the most accurate. The performance of this detector was assessed by means of experimental mechanical recordings of cardiac tissue of the right and left atria. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems)

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14 pages, 3039 KiB

Open AccessArticle

A New 12-Lead ECG Signals Fusion Method Using Evolutionary CNN Trees for Arrhythmia Detection

by Maytham N. Meqdad, Fardin Abdali-Mohammadi and Seifedine Kadry

Mathematics 2022, 10(11), 1911; https://doi.org/10.3390/math10111911 - 02 Jun 2022

Cited by 8 | Viewed by 2220

Abstract

The 12 leads of electrocardiogram (ECG) signals show the heart activities from different angles of coronal and axial planes; hence, the signals of these 12 leads have functional dependence on each other. This paper proposes a novel method for fusing the data of 12-lead ECG signals to diagnose heart problems. In the first phase of the proposed method, the time-frequency transform is employed to fuse the functional data of leads and extract the frequency data of ECG signals in 12 leads. After that, their dependence is evaluated through the correlation analysis. In the second phase, a structural learning method is adopted to extract the structural data from these 12 leads. Moreover, deep convolutional neural network (CNN) models are coded in this phase through genetic programming. These trees are responsible for learning deep structural features from functional data extracted from 12 leads. These trees are upgraded through the execution of the genetic programming (GP) algorithm to extract the optimal features. These two phases are used together to fuse the leads of ECG signals to diagnose various heart problems. According to the test results on ChapmanECG, including the signals of 10,646 patients, the proposed method enjoys the mean accuracy of 97.60% in the diagnosis of various types of arrhythmias in the Chapman dataset. It also outperformed the state-of-the-art methods. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems)

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16 pages, 12001 KiB

Open AccessArticle

Multi-Weighted Partial Domain Adaptation for Sucker Rod Pump Fault Diagnosis Using Motor Power Data

by Dezhi Hao and Xianwen Gao

Mathematics 2022, 10(9), 1519; https://doi.org/10.3390/math10091519 - 02 May 2022

Cited by 2 | Viewed by 1523

Abstract

Motor power curves (MPCs) have received great attention for use in diagnosing the working conditions of sucker rod pumping systems (SRPSs) because of their advantages in accessibility and real-time performance. However, existing MPC-based approaches mostly need a rigorous assumption that the MPC instances of different working conditions are sufficient, which does not hold in industrial scenarios. To this end, this paper proposes an unsupervised fault diagnosis methodology to leverage readily available dynamometer cards (DCs) to diagnose collected unlabeled MPCs. Firstly, a mathematical model of the SRPS is presented to convert actual DCs to MPCs. Secondly, a novel diagnostic methodology based on adversarial domain adaptation is proposed for the problem of data distribution discrepancy across the collected and converted MPCs. Specifically, the collected unlabeled MPCs may only cover a subset of the working conditions of the abundant DCs, which will easily cause negative transfer and lead to dramatic performance degradation. This proposed methodology employs class-level and distribution-level weighting strategies so as to guide the network to focus on the instances from shared categories and down-weight the outlier ones. Validation experiments are performed to evaluate the mathematical model and the diagnostic methodology with a set of actual MPCs collected by a self-developed device. The experimental result indicates that the accuracy of the proposed algorithm can reach

99.3 %

in diagnosing actual MPCs when only labeled DCs and unlabeled actual MPCs are used. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems)

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20 pages, 3641 KiB

Open AccessArticle

Siamese Neural Networks for Damage Detection and Diagnosis of Jacket-Type Offshore Wind Turbine Platforms

by Joseph Baquerizo, Christian Tutivén, Bryan Puruncajas, Yolanda Vidal and José Sampietro

Mathematics 2022, 10(7), 1131; https://doi.org/10.3390/math10071131 - 01 Apr 2022

Cited by 5 | Viewed by 3107

Abstract

Offshore wind energy is increasingly being realized at deeper ocean depths where jacket foundations are now the greatest choice for dealing with the hostile environment. The structural stability of these undersea constructions is critical. This paper states a methodology to detect and classify damage in a jacket-type support structure for offshore wind turbines. Because of the existence of unknown external disturbances (wind and waves), standard structural health monitoring technologies, such as guided waves, cannot be used directly in this application. Therefore, using vibration-response-only accelerometer measurements, a methodology based on two in-cascade Siamese convolutional neural networks is proposed. The first Siamese network detects the damage (discerns whether the structure is healthy or damaged). Then, in case damage is detected, a second Siamese network determines the damage diagnosis (classifies the type of damage). The main results and claims of the proposed methodology are the following ones: (i) It is solely dependent on accelerometer sensor output vibration data, (ii) it detects damage and classifies the type of damage, (iii) it operates in all wind turbine regions of operation, (iv) it requires less data to train since it is built on Siamese convolutional neural networks, which can learn from very little data compared to standard machine/deep learning algorithms, (v) it is validated in a scaled-down experimental laboratory setup, and (vi) its feasibility is demonstrated as all computed metrics (accuracy, precision, recall, and F1 score) for the obtained results remain above 96%. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems)

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38 pages, 7322 KiB

Open AccessArticle

Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

by Jersson X. Leon-Medina, Núria Parés, Maribel Anaya, Diego A. Tibaduiza and Francesc Pozo

Mathematics 2022, 10(1), 29; https://doi.org/10.3390/math10010029 - 22 Dec 2021

Cited by 7 | Viewed by 3336

Abstract

The classification and use of robust methodologies in sensor array applications of electronic noses (ENs) remain an open problem. Among the several steps used in the developed methodologies, data preprocessing improves the classification accuracy of this type of sensor. Data preprocessing methods, such as data transformation and data reduction, enable the treatment of data with anomalies, such as outliers and features, that do not provide quality information; in addition, they reduce the dimensionality of the data, thereby facilitating the tasks of a machine learning classifier. To help solve this problem, in this study, a machine learning methodology is introduced to improve signal processing and develop methodologies for classification when an EN is used. The proposed methodology involves a normalization stage to scale the data from the sensors, using both the well-known

min - max

approach and the more recent mean-centered unitary group scaling (MCUGS). Next, a manifold learning algorithm for data reduction is applied using uniform manifold approximation and projection (UMAP). The dimensionality of the data at the input of the classification machine is reduced, and an extreme learning machine (ELM) is used as a machine learning classifier algorithm. To validate the EN classification methodology, three datasets of ENs were used. The first dataset was composed of 3600 measurements of 6 volatile organic compounds performed by employing 16 metal-oxide gas sensors. The second dataset was composed of 235 measurements of 3 different qualities of wine, namely, high, average, and low, as evaluated by using an EN sensor array composed of 6 different sensors. The third dataset was composed of 309 measurements of 3 different gases obtained by using an EN sensor array of 2 sensors. A 5-fold cross-validation approach was used to evaluate the proposed methodology. A test set consisting of 25% of the data was used to validate the methodology with unseen data. The results showed a fully correct average classification accuracy of 1 when the MCUGS, UMAP, and ELM methods were used. Finally, the effect of changing the number of target dimensions on the reduction of the number of data was determined based on the highest average classification accuracy. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems)

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17 pages, 5108 KiB

Open AccessArticle

Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network

by Feiyue Deng, Yan Bi, Yongqiang Liu and Shaopu Yang

Mathematics 2021, 9(23), 3035; https://doi.org/10.3390/math9233035 - 26 Nov 2021

Cited by 15 | Viewed by 1852

Abstract

Remaining useful life (RUL) prediction of key components is an important influencing factor in making accurate maintenance decisions for mechanical systems. With the rapid development of deep learning (DL) techniques, the research on RUL prediction based on the data-driven model is increasingly widespread. Compared with the conventional convolution neural networks (CNNs), the multi-scale CNNs can extract different-scale feature information, which exhibits a better performance in the RUL prediction. However, the existing multi-scale CNNs employ multiple convolution kernels with different sizes to construct the network framework. There are two main shortcomings of this approach: (1) the convolution operation based on multiple size convolution kernels requires enormous computation and has a low operational efficiency, which severely restricts its application in practical engineering. (2) The convolutional layer with a large size convolution kernel needs a mass of weight parameters, leading to a dramatic increase in the network training time and making it prone to overfitting in the case of small datasets. To address the above issues, a multi-scale dilated convolution network (MsDCN) is proposed for RUL prediction in this article. The MsDCN adopts a new multi-scale dilation convolution fusion unit (MsDCFU), in which the multi-scale network framework is composed of convolution operations with different dilated factors. This effectively expands the range of receptive field (RF) for the convolution kernel without an additional computational burden. Moreover, the MsDCFU employs the depthwise separable convolution (DSC) to further improve the operational efficiency of the prognostics model. Finally, the proposed method was validated with the accelerated degradation test data of rolling element bearings (REBs). The experimental results demonstrate that the proposed MSDCN has a higher RUL prediction accuracy compared to some typical CNNs and better operational efficiency than the existing multi-scale CNNs based on different convolution kernel sizes. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems)

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26 pages, 7563 KiB

Open AccessArticle

Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines

by Asif Khan, Hyunho Hwang and Heung Soo Kim

Mathematics 2021, 9(18), 2336; https://doi.org/10.3390/math9182336 - 21 Sep 2021

Cited by 19 | Viewed by 3889

Abstract

As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter’s data clusters are more distinct than the former’s. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems)

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23 pages, 7592 KiB

Open AccessArticle

Development and Validation of a Post-Earthquake Safety Assessment System for High-Rise Buildings Using Acceleration Measurements

by Koji Tsuchimoto, Yasutaka Narazaki and Billie F. Spencer, Jr.

Mathematics 2021, 9(15), 1758; https://doi.org/10.3390/math9151758 - 26 Jul 2021

Cited by 7 | Viewed by 2378

Abstract

After a major seismic event, structural safety inspections by qualified experts are required prior to reoccupying a building and resuming operation. Such manual inspections are generally performed by teams of two or more experts and are time consuming, labor intensive, subjective in nature, and potentially put the lives of the inspectors in danger. The authors reported previously on the system for a rapid post-earthquake safety assessment of buildings using sparse acceleration data. The proposed framework was demonstrated using simulation of a five-story steel building modeled with three-dimensional nonlinear analysis subjected to historical earthquakes. The results confirmed the potential of the proposed approach for rapid safety evaluation of buildings after seismic events. However, experimental validation on large-scale structures is required prior to field implementation. Moreover, an extension to the assessment of high-rise buildings, such as those commonly used for residences and offices in modern cities, is needed. To this end, a 1/3-scale 18-story experimental steel building tested on the shaking table at E-Defense in Japan is considered. The importance of online model updating of the linear building model used to calculate the Damage Sensitive Features (DSFs) during the operation is also discussed. Experimental results confirm the efficacy of the proposed approach for rapid post-earthquake safety evaluation for high-rise buildings. Finally, a cost-benefit analysis with respect to the number of sensors used is presented. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems)

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