PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

BACKGROUND AND MOTIVATION

Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias.

METHODS

Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework.

FINDINGS AND CONCLUSIONS

UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

OBJECTIVES

In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management.

METHODS

We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate.

RESULTS

From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context.

CONCLUSIONS

Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.

Railway Intrusion Events Classification and Location Based on Deep Learning in Distributed Vibration Sensing

With the rapid development of the high-speed railway industry, the safety of railway operations is becoming increasingly important. As a symmetrical structure, traditional manual patrol and camera surveillance solutions on both sides of the railway require enormous manpower and material resources and are highly susceptible to weather and electromagnetic interference. In contrast, a distributed fiber optic vibration sensing system can be continuously monitored and is not affected by electromagnetic interference to false alarms. However, it is still a challenge to identify the type of intrusion event along the fiber optic cable. In this paper, a railway intrusion event classification and location scheme based on a distributed vibration sensing system was proposed. In order to improve the accuracy and reliability of the recognition, a 1 DSE-ResNeXt+SVM method was demonstrated. Squeeze-and-excitation blocks with attention mechanisms increased the classification ability by sifting through feature information without being influenced by non-critical information, while a support vector machine classifier can further improve the classification accuracy. The method achieved an accuracy of 96.0% for the identification of railway intrusion events with the field experiments. It illustrates that the proposed scheme can significantly improve the safety of railway operations and reduce the loss of personnel and property safety.

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. However, gradient-based methods have major drawbacks such as stucking at local minimums in multi-objective cost functions, expensive execution time due to calculating gradient information with thousands of iterations and needing the cost functions to be continuous. Since training the ANNs and DLs is an NP-hard optimization problem, their structure and parameters optimization using the meta-heuristic (MH) algorithms has been considerably raised. MH algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of layers, number of neurons, learning rate, etc.). This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms. In this paper, we have reviewed the latest developments in the use of MH algorithms in the DL and ANN methods, presented their disadvantages and advantages, and pointed out some research directions to fill the gaps between MHs and DL methods. Moreover, it has been explained that the evolutionary hybrid architecture still has limited applicability in the literature. Also, this paper classifies the latest MH algorithms in the literature to demonstrate their effectiveness in DL and ANN training for various applications. Most researchers tend to extend novel hybrid algorithms by combining MHs to optimize the hyper-parameters of DLs and ANNs. The development of hybrid MHs helps improving algorithms performance and capable of solving complex optimization problems. In general, the optimal performance of the MHs should be able to achieve a suitable trade-off between exploration and exploitation features. Hence, this paper tries to summarize various MH algorithms in terms of the convergence trend, exploration, exploitation, and the ability to avoid local minima. The integration of MH with DLs is expected to accelerate the training process in the coming few years. However, relevant publications in this way are still rare.

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0

BACKGROUND

COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization.

METHOD

ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted.

RESULTS

Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions.

CONCLUSIONS

Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer

Coronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription–polymerase chain reaction (RT-PCR) test is used for the diagnosis of COVID-19 in national healthcare centers. Because the number of PCR test kits is often limited, it is sometimes difficult to diagnose the disease at an early stage. However, X-ray technology is accessible nearly all over the world, and it succeeds in detecting symptoms of COVID-19 more successfully. Another disease which affects people’s lives to a great extent is colorectal cancer. Tissue microarray (TMA) is a technological method which is widely used for its high performance in the analysis of colorectal cancer. Computer-assisted approaches which can classify colorectal cancer in TMA images are also needed. In this respect, the present study proposes a convolutional neural network (CNN) classification approach with optimized parameters using gradient-based optimizer (GBO) algorithm. Thanks to the proposed approach, COVID-19, normal, and viral pneumonia in various chest X-ray images can be classified accurately. Additionally, other types such as epithelial and stromal regions in epidermal growth factor receptor (EFGR) colon in TMAs can also be classified. The proposed approach was called COVID-CCD-Net. AlexNet, DarkNet-19, Inception-v3, MobileNet, ResNet-18, and ShuffleNet architectures were used in COVID-CCD-Net, and the hyperparameters of this architecture was optimized for the proposed approach. Two different medical image classification datasets, namely, COVID-19 and Epistroma, were used in the present study. The experimental findings demonstrated that proposed approach increased the classification performance of the non-optimized CNN architectures significantly and displayed a very high classification performance even in very low value of epoch.

Deep learning for assessing liver fibrosis based on acoustic nonlinearity maps: an in vivo study of rabbits

This study aimed to assess liver fibrosis in rabbits by deep learning models based on acoustic nonlinearity maps. Injection of carbon tetrachloride was used to induce liver fibrosis. Acoustic nonlinearity maps, which were built by data of echo signals, were used as input data for deep learning model. Convolutional neural network (CNN), CNN combined with support vector machine (SVM), CNN combined with random forest and CNN combined with logistic regression were used as deep learning model. Nested 10-fold cross-validation was used to search hyperparameters and evaluate performance of models. Histologic examination of liver specimens of the rabbits was performed to evaluate the fibrosis stage. Receiver operator characteristic curve and area under curve (AUC) were used for estimating the probability of the correct prediction of liver fibrosis stages. A total of 600 acoustic nonlinearity maps were used. Model of CNN combined with SVM demonstrated the best diagnostic performance compared with all other methods for diagnosis of significant fibrosis (≥F2, AUC = 0.82), advanced fibrosis (≥F3, AUC = 0.88) and cirrhosis (F4, AUC = 0.90). Model of CNN showed the second highest AUCs. The deep learning model based on acoustic nonlinearity maps demonstrated potential for evaluation of liver fibrosis.

A deep neural network for the classification of epileptic seizures using hierarchical attention mechanism

Electroencephalogram (EEG) is a common diagnostic tool for measuring the seizure activity of the brain. There are many deep learning techniques introduced to analyze EEG. These methods show phenomenal results, although they are limited to computational complexity. Our objective was to develop a novel algorithm that gives maximum classification accuracy with a minor computational complexity. In this view, we have introduced a novel convolutional architecture with an integration of a hierarchical attention mechanism. The model comprises three parts: Feature extraction layer, which uses to extract the convoluted feature map; hierarchical attention layer, which is used to obtain weighted hierarchical feature map; classification layer, which uses weighted features for classification of healthy and seizure subjects. The proposed model can extract significant information from the EEG signal to classify seizure subjects, and it is compared with a few existing deep convolutional algorithms through experimentation. The experimental outcomes show that the proposed model has higher accuracy with less computational time.

A Deep Learning-Based Model for the Automated Assessment of the Activity of a Single Worker

Nowadays, it is necessary to verify the accuracy of servicing work, undertaken by new employees, within a manufacturing company. A gap in the research has been observed in effective methods to automatically evaluate the work of a newly employed worker. The main purpose of the study is to build a new, deep learning model, in order to automatically assess the activity of the single worker. The proposed approach integrates the methods known as CNN, CNN + SVM, CNN + R-CNN, four new algorithms and a piece of work from a selected company, using this as an own-created dataset, in order to create a solution enabling assessment of the activity of single workers. Data were collected from an operational manufacturing cell without any guided or scripted work. The results reveal that the model developed is able to accurately detect the correctness of the work process. The model’s accuracy mostly exceeds current state-of-the-art methods for detecting work activities in manufacturing. The proposed two-stage approach, firstly, assigning the appropriate graphic instruction to a given employee’s activity using CNN and then using R-CNN to isolate the object from the reference frames, yields 94.01% and 73.15% accuracy of identification, respectively.