Deep Convolutional Generative Adversarial Network for Improved Cardiac Image Classification in Heart Disease Diagnosis.
Heart disease is a fatal disease that causes significant mortality rates worldwide. The accurate and early detection of heart diseases is the most challenging task to save valuable lives. To avoid these issues, the Deep Convolutional Generative Adversarial Network (DCGAN) model is proposed that gene...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2146 - 2170 |
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| Autores principales: | , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278979&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278979 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278979 187278979 187278979 10.1007/s10278-024-01343-z 187278979 ppf: 2146 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Convolutional Generative Adversarial Network for Improved Cardiac Image Classification in Heart Disease Diagnosis. aug: au: S, Gurusubramani B, Latha affil: https://ror.org/01vv1bg04 Department of Computer Science and Engineering, Sri Sairam Engineering College, Anna University, Chennai, India sug: subj: Deep Learning Generative Adversarial Networks Image Interpretation, Computer Assisted Image Processing, Computer Assisted Heart Diseases Diagnosis Artificial Intelligence Diagnostic Imaging Human Resource Databases Algorithms Neural Networks (Computer) Sensitivity and Specificity Validity Precision Time Machine Learning Magnetic Resonance Imaging Information Science Computer Simulation Benchmarking ab: Heart disease is a fatal disease that causes significant mortality rates worldwide. The accurate and early detection of heart diseases is the most challenging task to save valuable lives. To avoid these issues, the Deep Convolutional Generative Adversarial Network (DCGAN) model is proposed that generates synthetic cardiac images. Here, two types of heart disease datasets such as the Sunnybrook Cardiac Dataset (SCD) and the Automated Cardiac Diagnosis Challenge (ACDC) dataset are selected to choose real cardiac images for implementation. The quality and consistency of the cardiac images are enhanced by preprocessed real cardiac images. In the DCGAN model, the generator is used for converting real cardiac images into synthetic images and the discriminator is responsible for differentiating real and synthetic cardiac images by binary classification decisions. To enhance the model's robustness and generalization ability, diverse augmentation techniques are implemented. The VGG16 model is applied in this paper for the image classification task and fine-tuned its parameters to optimize model convergence. For experimental validation, some of the significance metrics such as accuracy, precision, diagnostic time, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), false positive rate (FPR), false negative rate (FNR), and mean squared error (MSE) are utilized. The extensive experimental evaluations are carried out based on this metrics and attained a performance rate of the proposed method as 98.83%, 1.17%, 3.2%, 41.78, 4.52, 0.932, and 1.6 s from accuracy, FPR, FNR, PSNR, MSE, SSIM, and diagnostic time, respectively. The experimental evaluation results demonstrate that the proposed heart disease diagnosis model attains superior performances than state-of-the-art methods. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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