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Original research

Detection and comparison of Diabetic Maculopathy using C-Means Clustering Algorithm and Watershed Algorithm

Abstract

Aim: The aim of this research work is for the presence of Novel Diabetic Maculopathy Detection using modern algorithms, and comparing the Peak Signal to Noise Ratio (PSNR) between the C-Means clustering Algorithms and Watershed Algorithm. Materials and Methods: The sample images were taken from kaggle’s website. Samples were considered as (N=24) for C-Means Clustering Algorithm and (N=24) for Watershed algorithm in accordance with total sample size calculated using clinicalc.com by keeping alpha error-threshold value 0.05, enrollment ratio as 0.1, 95% confidence interval, G power as 80%. The Peak Signal to Noise Ratio was calculated by using the MATLAB Programming with a standard data set. Results: Comparison of PSNR is done by independent sample t-test using SPSS software. There is a statistical insignificant difference between C-Means Clustering Algorithm and Watershed algorithm with p=0.11, p>0.05 (PSNR = 35.3411) showed better results in comparison to Watershed Algorithm (PSNR =9.7420). Conclusion: C-Means Clustering Algorithms were found to give higher PSNR than in Watershed Algorithms for the Novel Diabetic Maculopathy Detection.

Imprint

Farheen Naz, Jenila Rani D, R. Rajakumari. Detection and comparison of Diabetic Maculopathy using C-Means Clustering Algorithm and Watershed Algorithm. Cardiometry; Issue 25; December 2022; p.845-851; DOI: 10.18137/cardiometry.2022.25.845851; Available from: https://www.cardiometry.net/issues/no25-december-2022/detection-comparison-diabetic

Keywords

Novel Diabetic Maculopathy Detection,  Machine learning,  C-Means Clustering Algorithm,  Watershed Algorithm,   MATLAB Programming,  Peak Signal to Noise Ratio (PSNR)
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