Internet of medical things (IoMT) plays an important role nowadays to support healthcare system. The hospital equipment’s called as medical things are now connected to the cloud for getting many useful services. The data generated from the equipments are sent to the cloud for getting the desired service. In current scenario, most hospitals collect many images using equipments, but these equipments have less computational capability to process the huge generated data. In this work, one such equipment is considered which can take the human eye images and send the images to the cloud for detection of cherry red spot (CRS). CRS disease in eyes is considered as one of the very dangerous disease. The early diagnosis of CRS disease needs to be focused in order to avoid any adverse effect on human body. In this paper, a machine intelligence based model is proposed to detect the CRS disease areas in the human eyes by analyzing several CRS disease images using IoMT. The proposed approach is mainly focused on fuzzy rule-based mechanism to carry out the identification of such affected area in the eyes in cloud layer. From the results, it is observed that the CRS disease areas in the eyes are detected well with better detection accuracy and lower detection error than k-means algorithm. This approach will help the doctors to track the exact position of the affected areas in the eye for its diagnosis. The simulation is performed using socket programming written in Python 3 where a cloud server and a client device are created and images are sent from the client device to the server, and afterwards the detection of CRS is performed at the server using MATLAB R2015b. The proposed method is able to provide better performance in terms of detection accuracy, detection error and processing time as 94.67%, 5.33% and 1.1481% units respectively on an average case scenario.

如今，医疗物联网 (IoMT) 在支持医疗保健系统方面发挥着重要作用。医院设备被称为医疗物，现在已经连接到云端，以获得许多有用的服务。设备生成的数据被发送到云端以获得所需的服务。在当前场景下，大多数医院使用设备采集大量图像，但这些设备处理生成的海量数据的计算能力较差。在这项工作中，考虑了一种可以拍摄人眼图像并将图像发送到云端以检测樱桃红斑（CRS）的设备。眼部CRS疾病被认为是一种非常危险的疾病。CRS疾病的早期诊断需要重点关注，以免对人体产生任何不良影响。在本文中，提出了一种基于机器智能的模型，通过使用 IoMT 分析多个 CRS 疾病图像来检测人眼中的 CRS 疾病区域。所提出的方法主要集中在基于模糊规则的机制上，在云层中对眼睛中的这种受影响区域进行识别。从结果中可以看出，与k-means算法相比，眼睛中CRS疾病区域的检测效果更好，检测精度更高，检测误差更低。这种方法将帮助医生跟踪眼睛中受影响区域的确切位置以进行诊断。使用 Python 3 编写的套接字编程执行模拟，其中创建云服务器和客户端设备，并将图像从客户端设备发送到服务器，然后使用 MATLAB R2015b 在服务器上执行 CRS 检测。