Intelligent Framework for Smart Health Application using Image Analysis and Knowledge Relegation Approach

Abstract

The future direction of modern medicine is toward “smart healthcare,” which incorporates a new generation of information technology to meet patient needs individually while increasing the effectiveness of medical care. This greatly improves the patient experience with medical and health services. Nowadays, due to people's lifestyles, diabetic retinopathy is one of the most serious health issues they confront. A deviation from the norm in which long-term diabetes affects the human retina is called diabetic retinopathy (DR). Diabetes is a chronic condition related to an expanding measure of glucose levels. As the degree of glucose builds, a few adjustments happen in the veins of the retina. Patients' vision may begin to deteriorate as their diabetes progresses, resulting in diabetic retinopathy. It is exceptionally far-reaching among moderately aged and older individuals. Thus there is a need to detect diabetic retinopathy at an early stage automatically. This study aims to build an intelligent framework that uses fundus images of the eye (retina) and performs image analysis to extract the features. Images are trained by the knowledge relegation approach, and the severity of the DR is classified using K-nearest neighbors. The proposed model achieved a test accuracy of 99%, 61%, 100%, 94%, and 88% for each of the five classes of diabetic retinopathy: proliferative diabetic retinopathy, no diabetic retinopathy, mild diabetic retinopathy, moderate diabetic retinopathy, and severe diabetic retinopathy.