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. 2023 Feb 18;13(4):775.
doi: 10.3390/diagnostics13040775.

IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks

Chitra Balakrishnan  1 , V D Ambeth Kumar  2
Affiliations

IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks

Chitra Balakrishnan et al. Diagnostics (Basel). .

Abstract

Cardiovascular diseases currently present a key health concern, contributing to an increase in death rates worldwide. In this phase of increasing mortality rates, healthcare represents a major field of research, and the knowledge acquired from this analysis of health information will assist in the early identification of disease. The retrieval of medical information is becoming increasingly important to make an early diagnosis and provide timely treatment. Medical image segmentation and classification is an emerging field of research in medical image processing. In this research, the data collected from an Internet of Things (IoT)-based device, the health records of patients, and echocardiogram images are considered. The images are pre-processed and segmented, and then further processed using deep learning techniques for classification as well as forecasting the risk of heart disease. Segmentation is attained via fuzzy C-means clustering (FCM) and classification using a pretrained recurrent neural network (PRCNN). Based on the findings, the proposed approach achieves 99.5% accuracy, which is higher than the current state-of-the-art techniques.

Keywords: FCM; IoT; PRCNN; cardiovascular disease; echocardiogram images; risk prediction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Sensor function in IoT enabled Medical Device.
Figure 2
Figure 2
Overall workflow of proposed work.
Figure 3
Figure 3
Outline of deep CNN with RELU.
Figure 4
Figure 4
Input image.
Figure 5
Figure 5
Output of the pre-processed image.
Figure 6
Figure 6
FCM-based segmented images.
Figure 7
Figure 7
Comparison of accuracy.
Figure 8
Figure 8
Comparison of sensitivity.
Figure 9
Figure 9
Comparison of specificity.
See this image and copyright information in PMC

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