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Diabetic Cardiomyopathy (DCM) is a serious form of cardiovascular disease in diabetic patients, characterized by structural and functional changes in the heart that frequently lead to heart failure. Coronary artery plaque formation is an important aspect of DCM, which exacerbates the condition by restricting blood flow and adding to the complexity of diabetes-related heart disease. To address this issue, we present CARDIO-VGTS-Net, a novel framework that combines the deep learning power of the VGG-19 Convolutional Neural Network with the nuanced texture analysis capabilities of the Gray-Level Co-occurrence Matrix (GLCM). This integrated approach efficiently extracts and combines intricate image features, allowing for the precise detection and characterization of plaque formations and other DCM-related abnormalities in microscopic images of blood vessels. Our methodology, by providing a more nuanced understanding and accurate diagnosis of DCM, promises to improve early detection and inform targeted treatment strategies, representing a significant advancement in the management of cardiovascular complications in diabetic patients.

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An Integrated Framework for Early Detection of Diabetic Cardiomyopathy Using CARDIO-VGTS-Net Model

  • 会议论文

  • 发表日期：

    2026年

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  • Punganuru Swathi,
  • M. Gunasekaran

摘要

Diabetic Cardiomyopathy (DCM) is a serious form of cardiovascular disease in diabetic patients, characterized by structural and functional changes in the heart that frequently lead to heart failure. Coronary artery plaque formation is an important aspect of DCM, which exacerbates the condition by restricting blood flow and adding to the complexity of diabetes-related heart disease. To address this issue, we present CARDIO-VGTS-Net, a novel framework that combines the deep learning power of the VGG-19 Convolutional Neural Network with the nuanced texture analysis capabilities of the Gray-Level Co-occurrence Matrix (GLCM). This integrated approach efficiently extracts and combines intricate image features, allowing for the precise detection and characterization of plaque formations and other DCM-related abnormalities in microscopic images of blood vessels. Our methodology, by providing a more nuanced understanding and accurate diagnosis of DCM, promises to improve early detection and inform targeted treatment strategies, representing a significant advancement in the management of cardiovascular complications in diabetic patients.