Health System for Exercise Rehabilitation Detection Retina Images and IoT Blockchain

B Sriman, Seetha R, Mustafa Nawaz S M, V Velmurugan, N R Sathis Kumar, S H Annie Silviya
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Abstract

Smart Healthcare which is based on deep learning is becoming increasingly popular because of its practical applications and has grown popular after its incorporation with IoT. Degenerative eye disease is the main factor of blindness in people of working age. Asian nations with large populations, like India and China, are on the edge of a diabetes epidemic. In terms of medical screening and diagnosis, a large number of diabetes patients posed a huge problem for skilled clinicians. The idea is to employ deep learning algorithms to detect blind spots in the eye and estimate the severity of the stage. We present an optimum approach for detecting blindness in retinal images based on recently released pre-trained EfficientNet models, as well as a comparative assessment of many innovative neural network models, in this study. On a benchmark dataset of retina images obtained by diagnostic imaging at various imaging phases, our EfficientNet-B5-based model assessment performs better than CNN and ResNet50 models.
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运动康复检测视网膜图像和物联网区块链健康系统
基于深度学习的智能医疗因其实际应用而越来越受欢迎,并在与物联网结合后变得越来越受欢迎。退行性眼病是造成劳动年龄人群失明的主要因素。印度和中国等人口众多的亚洲国家正处于糖尿病流行的边缘。在医学筛查和诊断方面,大量的糖尿病患者给熟练的临床医生带来了巨大的问题。这个想法是使用深度学习算法来检测眼睛中的盲点,并估计该阶段的严重程度。在这项研究中,我们提出了一种基于最近发布的预训练effentnet模型的视网膜图像失明检测的最佳方法,并对许多创新的神经网络模型进行了比较评估。在不同成像阶段通过诊断成像获得的视网膜图像的基准数据集上,我们基于efficientnet - b5的模型评估优于CNN和ResNet50模型。
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