Industrial Internet of Things (IIoT) with Cloud Teleophthalmology-Based Age-Related Macular Degeneration (AMD) Disease Prediction Model

Industrial Internet of things (IIoT) utilizes smart sensors and actuators for enhancing manufacturing and industrial processes. Due to the advanced technological developments in healthcare industry, it has been proved that the primary detection of chronic diseases, namely, diabetic retinopathy (DR) as well as age-related macular degeneration (AMD), is capable of preventing loss of vision. In this study, a scalable cloud-oriented teleophthalmology structure by an Internet of medical things (IoMT) to detect AMD is projected. In the presented system, patient’s wearable camera for transmitting the retinal fundus photographs for a secured cloud drive storage for diagnosing the severity of disease as well as predictive progression examination. A projected optimal generative adversarial network (OGAN) helps to investigate the images to find as well as to compute AMD disease severity. The GAN would be optimized with the application of a bat method. The performance of the proposed OGAN model has been validated using a set of benchmark images. A set of three measures used to examine the results are sensitivity, specificity, and accuracy. The experimental outcome showed the superior performance of the proposed model over the compared methods by attaining a maximum accuracy of 98.03%.