C-CADZ: computational intelligence system for coronary artery disease detection using Z-Alizadeh Sani dataset

Gupta, Ankur; Kumar, Rahul; Arora, Harkirat Singh; Raman, Balasubramanian

doi:10.1007/s10489-021-02467-3

Cited by 30 publications

(10 citation statements)

References 57 publications

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“…Nevertheless, more time was consumed during the node's processing in the GraphECV and the model's speed was lessened. (Gupta et al, 2022) [9] established a Computer-intelligence system for CAD detection on the Z-AlizadehSani dataset (C-CADZ). For FS, C-CADZ wielded Fixed Analysis of Mixed Data (FAMD) and was trained by means of the RF and Extra Trees (ET) classifiers.…”

Section: Related Workmentioning

confidence: 99%

Echocardiography Based Cardiac Ischemic Detection Using Red-CNN Classifier

Jyothi P

2024

jes

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One of the most vital and hardest-working organs of the human body is the Heart. Nevertheless, a condition termed Cardiac Ischemia or CAD is engendered by the inadequate supply of blood along with oxygen to the heart muscle. So, with the aid of a Regularisation Dropout-centered Convolutional Neural Network (ReD-CNN) classifier, an efficient CAD detection system is proposed. At first, the input videos and the data are amassed from the Echonet Dynamic dataset. Next, frame conversion is undergone by the input video. For further processing, keyframes are extracted and preprocessed after frame conversion. Afterward, by means of a Contraction Expansion-Residual Network (CE-ResNet), the heart chambers are segmented, and then the features are extracted. Simultaneously, utilizing the Feynman Kan Formalism-based Tissue Doppler Imaging (FKF-TDI) approach, wall motion is estimated. Meanwhile, the features are extracted from the data and grouped via Average Distance-centric Fuzzy C Means (AD-FCM). After that, mapping of the features extracted from the frames and the grouped features occurs and the resultant data is divided into training, testing, along with validation stages. Then, utilizing the Cosine Similarity-centered Circulatory System Based Optimization (2(CS)BO) methodology, the Feature Selection (FS) is done. Therefore, to compute the Ischemic index, the features that are selected and the estimated wall motions are wielded. Lastly, for effective classification, the Ischemic index and the selected features are fed into the ReD-CNN classifier. Finally, to ascertain the superiority of this technique, the proposed system’s performance is contrasted with the prevailing methodologies.

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Section: Related Workmentioning

confidence: 99%

Echocardiography Based Cardiac Ischemic Detection Using Red-CNN Classifier

Jyothi P

2024

jes

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“…The medical industry has developed rapidly in recent years, especially in medical intelligent systems. Gupta A, Kumar R, Arora H S, et al [7] proposed the computational intelligence system C-CADZ for the diagnosis of coronary artery disease, and verified the data processing function of C-CADZ with the data set. Abbasian Ardakani A, Acharya U 2.…”

Section: Introductionmentioning

confidence: 98%

Development of a Geometric Cutting Aided System for Tin-sprayed Steel Mesh

Luo

Gui

Ding

2022

J. Phys.: Conf. Ser.

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The design of tin-sprayed steel mesh is particularly important in the production of tin-sprayed steel mesh, which is the key to ensuring the function and quality of the tin-sprayed steel mesh. At present, the tin-sprayed steel mesh is still manually designed, and the design process is error-prone and inefficient. Therefore, a geometric cutting assistance system for tin-sprayed steel mesh of the combiner based on AutoCAD and ObjectARX was developed. The system adopts a parameterized design to realize the automatic operation of the steel mesh cutting process. First introduced the system development environment and composition architecture, and briefly explained the system interaction interface. Then it explains two key methods in the software system design and development process, including cutting quality control and finding the intersection of line segments. Finally, the actual case proves that the system greatly improves the efficiency and quality of steel mesh cutting.

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“…However, there are many problems in traditional computer systems. For example, a series of problems, such as how to improve the system performance, how to process data fast and stably, and how to maintain high reliability and maintainability, need to be solved or optimized by these new methods, so that distributed computing can play a greater role and provide people with more service requirements to meet the market development requirements [3][4].…”

Section: Introductionmentioning

confidence: 99%

Distributed Computing Collaborative CAD System Based on COM / DCOM

2022

DPS

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With the rapid development of China's economy, there are more and more distributed computing systems, which are also widely used in actual production. The main research direction of this paper is the distributed computing collaborative CAD system based on COM / NASD system. The software is based on COM / DCOM, and studies the system through collaborative design, simulation and testing. In this process, we first need to build the model. Then, according to the actual situation, the corresponding algorithm program is established to simulate the running environment of the whole module and set the parameter variables. Secondly, the components are assembled and connected with the system according to the requirements. Finally, the system is tested as a whole. The test results show that the system has high data processing efficiency, fast system response time and low delay, which indicates that the system has good comprehensive performance.

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C-CADZ: computational intelligence system for coronary artery disease detection using Z-Alizadeh Sani dataset

Cited by 30 publications

References 57 publications

Echocardiography Based Cardiac Ischemic Detection Using Red-CNN Classifier

Echocardiography Based Cardiac Ischemic Detection Using Red-CNN Classifier

Development of a Geometric Cutting Aided System for Tin-sprayed Steel Mesh

Distributed Computing Collaborative CAD System Based on COM / DCOM

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