Enhancing Remote Healthcare with Internet of Things

This study describes a complete technique for accurately identifying retinal illnesses, with an emphasis on diabetic retinopathy (DR), as well as the facilitation of remote healthcare access using an Internet of Things (IoT) framework. The methodology comprises a methodical series of processes, beginning with establishing a diversified Retinal Fundus Database and ending with thorough pre-processing utilizing the Wiener Filter to enhance image quality. The Graph Cut Segmentation Algorithm precisely delineates regions and detects DR-affected areas. Advanced edge detection algorithms are used for boundary tracing, and Fuzzy C-Means clustering is used to refine DR-affected regions. Feature extraction is required for accurate diagnosis, using the Gray-Level Co-occurrence Matrix (GLCM) and convolutional neural network (CNN)-based feature learning. A multiclass support vector machine (SVM) classifier is used for disease classification. Remote access and data sharing are facilitated by IoT integration using a Raspberry Pi and MATLAB ThingSpeak. This methodology combines modern image processing, machine learning, and IoT technologies to give exact diagnostic results and increase patient access to healthcare.