Publication Details
Abstract
This project aims to investigate the feasibility and effectiveness of employing retinal imaging analysis via a camera connected to a Raspberry Pi to determine risk factors associated with various ocular disorders. The addition of Raspberry Pi makes it possible to take retinal pictures in a way that is both cheap and portable, allowing it to be used in places where resources are limited. Using image processing methods and machine learning algorithms, the retinal images are examined to identify potential risk factors, such as indicators of diabetic retinopathy or vascular anomalies. The suggested approach aims to enhance the early diagnosis and surveillance of ocular disorders, thereby enabling prompt intervention and preventive measures. Initial findings indicate favorable results regarding accuracy and efficiency, highlighting the potential of this methodology to transform preventive healthcare practices for ocular illnesses. This study investigates the use of ocular images to predict the risk of dementia. We aim to identify early symptoms of memory and reasoning impairments associated with dementia by examining these pictures. Our research focuses on developing a technique to help physicians identify individuals at risk for dementia early, thereby facilitating improved care and treatment. Advanced machine learning techniques can be used to analyze retinal images and identify subtle alterations that may indicate neurodegeneration. This method shows promise as a way to get early help and individualized healthcare plans that can help slow the course of dementia.