Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Sinha, Dibakar; Sharma, Ashish

doi:10.1117/1.jei.31.5.051405

Cited by 3 publications

(2 citation statements)

References 45 publications

(107 reference statements)

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“…Therefore, timely high-precision diagnostics of heart diseases, prevention and treatment at an early stage of the development of the disease acquire exceptional relevance. To improve the accuracy of diagnosing the state of the heart in recent decades, computer systems for automatic analysis [4][5][6] of electrocardiographic data obtained during the processing of an electrocardiographic signal have been widely used. Automatic analysis of electrocardiograms is a rather complex theoretical problem.…”

Section: Computational Methods Of the Cardiovascular Diseases Classif...mentioning

confidence: 99%

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Atamanyuk

Kondratenko

Havrysh

et al. 2023

Sci Rep

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Decision support systems can seriously help medical doctors in the diagnosis of different diseases, especially in complicated cases. This article is devoted to recognizing and diagnosing heart disease based on automatic computer processing of the electrocardiograms (ECG) of patients. In the general case, the change of the ECG parameters can be presented as a random sequence of the signals under processing. Developing new computational methods for such signal processing is an important research problem in creating efficient medical decision support systems. Authors consider the possibility of increasing the diagnostic accuracy of cardiovascular diseases by implementing of the new proposed computational method of information processing. This method is based on the generalized nonlinear canonical decomposition of a random sequence of the change of cardiogram parameters. The use of a nonlinear canonical model makes it possible to significantly simplify the maximum likelihood criterion for classifying diseases. This simplification is provided by the transition from a multi-dimensional distribution density of cardiogram parameters to a product of one-dimensional distribution densities of independent random coefficients of a nonlinear canonical decomposition. The absence of any restrictions on the class of random sequences under study makes it possible to achieve maximum accuracy in diagnosing cardiovascular diseases. Functional diagrams for implementing the proposed method reflecting the features of its application are presented. The quantitative parameters of the core of the computational diagnostic procedure can be determined in advance based on the preliminary statistical data of the ECGs for different heart diseases. That is why the developed method is quite simple in terms of computation (computing complexity, accuracy, computing time, etc.) and can be implemented in medical computer decision systems for monitoring cardiovascular diseases and for their diagnosis in real time. The results of the numerical experiment confirm the high accuracy of the developed method for classifying cardiovascular diseases.

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Section: Computational Methods Of the Cardiovascular Diseases Classif...mentioning

confidence: 99%

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Atamanyuk

Kondratenko

Havrysh

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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“…Sourabh Sharma et al [43] used Singular Value Decomposition (SVD) method for color image watermarking based on bee colony algorithm. Dibakar Sinha et al [44] used CNN for the detection of coronary artery disease. Idio Guarino et al [45] applied a different set of machine learning approaches for identifying intrusion detections with respect to various forms.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches

Karthiga

Durairaj²,

Nawaz

et al. 2022

Wireless Communications and Mobile Computing

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Detecting the attacks in Vehicular Ad hoc Network (VANET) system is very important to provide more secure and reliable communication between all vehicles in the system. In this article, an effective Intelligent Intrusion Detection System (IDS) is proposed using machine learning and deep learning approaches such as Adaptive Neuro Fuzzy Inference System (ANFIS) and Convolutional Neural Networks (CNN), respectively. The existing methods focus on detecting only the known attacks in VANET environment. This limitation is overcome by proposing the Intelligent IDS system using soft computing techniques. The proposed method consists of Known IDS (KIDS) and Unknown IDS (UIDS) modules, which detect both known attacks and unknown attacks. The KIDS module uses ANFIS classification module to detect the known malicious attacks, whereas the UIDS module uses a deep learning algorithm to detect the unknown attacks in VANET. Modified LeeNET (MLNET) architecture is proposed in this article to identify the type of unknown attacks. In this work, DoS attacks, Botnet attacks, PortScan attacks, and Brute Force attacks are detected using this hybrid learning algorithm. The proposed system obtains 96.9% of Pr, 98.3% of Se, 98.7% of Sp, and 98.6% of Acc and consumed 1.75 s for detecting the DoS attack on i-VANET dataset. The proposed system obtains 98.1% of Pr, 98.9% of Se, 98.1% of Sp, and 98.1% of Acc and consumed 0.95 s for detecting the Botnet attack. The proposed system obtains 98.7% of Pr, 99.1% of Se, 98.9% of Sp, and 99.2% of Acc and consumed 1.38 s for detecting the PortScan attack. The proposed system obtains 99.1of Pr, 97.8% of Se, 98.7% of Sp, and 98.5% of Acc and consumed 1.29 s for detecting the Brute Force attack. The developed methodology is tested on the real-time CIC-IDS 2017 dataset, and the experimental results are compared with other state-of-the-art methods.

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Heart Stroke Prediction Using Different Machine Learning Algorithms

Madduri,

Jonnalagadda,

Ayinapuru

et al. 2023

Algorithms for Intelligent Systems

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Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Cited by 3 publications

References 45 publications

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches

Heart Stroke Prediction Using Different Machine Learning Algorithms

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