A Comprehensive Image Processing Framework for Early Diagnosis of Diabetic Retinopathy

• Published in: Computers, materials & continua Vol. 81; no. 2; pp. 2665 - 2683
• Main Authors: Yadav, Kusum, Alharbi, Yasser, Alreshidi, Eissa Jaber, Alreshidi, Abdulrahman, Jain, Anuj Kumar, Jain, Anurag, Kumar, Kamal, Sharma, Sachin, Gupta, Brij B.
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2024
• Subjects: Algorithms; Clustering; Decision trees; Diabetes; Diabetic retinopathy; Diagnosis; Image processing; Image segmentation; Medical electronics; Medical imaging; Noise sensitivity; Particle swarm optimization
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2024.053565

In today’s world, image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical images. Automated analysis of medical images is essential for doctors, as manual investigation often leads to inter-observer variability. This research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing framework. The proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization (MIWPSO) and Fuzzy C-Means clustering (FCM) algorithms. Traditional FCM does not incorporate spatial neighborhood features, making it highly sensitive to noise, which significantly affects segmentation output. Our method incorporates a modified FCM that includes spatial functions in the fuzzy membership matrix to eliminate noise. The results demonstrate that the proposed FCM-MIWPSO method achieves highly precise and accurate medical image segmentation. Furthermore, segmented images are classified as benign or malignant using the Decision Tree-Based Temporal Association Rule (DT-TAR) Algorithm. Comparative analysis with existing state-of-the-art models indicates that the proposed FCM-MIWPSO segmentation technique achieves a remarkable accuracy of 98.42% on the dataset, highlighting its significant impact on improving diagnostic capabilities in medical imaging.