Detection Of Diabetic Retinopathy At Old-Age: A Mobile Application
Range of color is one of the qualitative major features used in the determination and improvement of quality and performance in the area of Image processing. The western side of the world is facing a problem of ‘Diabetic Retinopathy’ and although the modern tools in medical science have been introduced, operating a diabetic patient is a critical case in any dies is. In the era of digitization, it remains a public health issue, and we must make sure that patients should be treated at the optimal timing in case of eye-diesel problems. Most individuals are interested in user-friendly applications for undergoing treatments. This research work investigates the ‘early stage of diabetic retinopathy. In this model, Color features are extracted using the ‘Histogram’ for the targeted inputs. The only use of ‘Color Histogram’ is not enough for discriminating the various classes hence target input has to pass the Contrast limited adaptive histogram equalization (CLADE) from where we get different features including ‘standard deviation’, ‘mean’ which is essential while computing the pixel intensity range. These features combine to capture the unique information about the classes that need to distinguish. This research work adopts the (k- Nearest Neighbored) KNN algorithm to classify the features. The general way in this work, model KANE deals with the Histogram data. This article illustrates the ability of a kind classifier where promising results are obtained. The feasibility of this application was tested and confirmed by using live data/objects. Performance analysis is obtained by comparison with existing approaches, this new method results from an accuracy rate at the range of 95 to 98%. Our model will contribute to image analysis for human dishes detection especially DR at the early stage. Proper detection at an early stage is better for effective treatment and can control the growing stage towards blindness with diabetes cases.