Balakrishnan Chitra, Ambeth Kumar V D
Panimalar Engineering College, Anna University, Chennai 600123, India.
Computer Engineering, Mizoram University, Aizawl 796004, India.
Diagnostics (Basel). 2023 Feb 18;13(4):775. doi: 10.3390/diagnostics13040775.
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.
心血管疾病目前是一个关键的健康问题，导致全球死亡率上升。在这个死亡率不断上升的阶段，医疗保健是一个主要的研究领域，从这种健康信息分析中获得的知识将有助于疾病的早期识别。医学信息的检索对于早期诊断和及时治疗变得越来越重要。医学图像分割和分类是医学图像处理中一个新兴的研究领域。在本研究中，考虑了从基于物联网（IoT）的设备收集的数据、患者的健康记录以及超声心动图图像。对图像进行预处理和分割，然后使用深度学习技术进一步处理，以进行分类以及预测心脏病风险。通过模糊C均值聚类（FCM）实现分割，并使用预训练循环神经网络（PRCNN）进行分类。基于这些发现，所提出的方法实现了99.5%的准确率，高于当前的先进技术。
