An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

• Published in: BioMed research international Vol. 2021; no. 1
• Main Authors: Ghoushchi, Saeid Jafarzadeh, Ranjbarzadeh, Ramin, Dadkhah, Amir Hussein, Pourasad, Yaghoub, Bendechache, Malika
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Analysis; Angiography; Automation; Care and treatment; Clustering (Computers); Diabetes; Diabetes mellitus; Diabetic retinopathy; Diabetics; Diagnosis; Disease; Fluorescein; Genetic algorithms; Medical imaging; Medical imaging equipment; Methods; Neural networks; Patients; Prevention; Retina; Retinopathy; Risk factors; Visual impairment; Iran
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2021/5597222

The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.