| Autor: |
Jena, Kalyan Kumar, Bhoi, Sourav Kumar, Mohapatra, Debasis, Mallick, Chittaranjan, Sahoo, Kshira Sagar, Nayyar, Anand |
| Předmět: |
|
| Zdroj: |
Wireless Networks (10220038); Jan2023, Vol. 29 Issue 1, p247-265, 19p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Full text from SpringerLink