This study presents a novel method for heart disease classification by integrating the VGG 19 architecture with a 2D Convolutional Neural Network (CNN). This approach aims to improve diagnostic accuracy and reduce misdiagnosis risks. It involves preprocessing heart images, selecting relevant features, and applying classification algorithms. The method achieved a $\mathbf{9 0 \%}$ accuracy rate in data validation. It uses CNNs for detailed data analysis and comparison, and introduces a hybrid technique combining VGG 19 with a 2D CNN. Various classification models were tested, with the GLCM combined with GoogleNet emerging as the most effective for feature selection and accurate heart disease prediction.


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    Titel :

    Analyzing and Classification of Heart Disease using VGG19


    Beteiligte:
    Elavarasi, C (Autor:in) / Priya, M. (Autor:in)


    Erscheinungsdatum :

    06.11.2024


    Format / Umfang :

    793339 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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