A comprehensive computer-aided system for an early-stage diagnosis and classification of diabetic macular edema

Diabetic macular edema (DME) is a condition of retinal swelling due to the accumulation of leaked plasma in the extracellular space in the retina, known as the macula. DME is a chronic progressive retinal disorder that likely results in permanent complete blindness. The early signs of DME are not discernible by merely suspecting the fundus picture with the naked eye. Therefore, a computer-assisted diagnostic system could aid ophthalmologists in screening, diagnosing, and treating DME. This study proposes an efficient and complete model for early diagnosis and staging of DME. To overcome the challenges in precise localization of fovea, blood vessel network extraction, and accurate lesion segmentation, improved image relative subtraction, Gabor wavelet filter, and novel advanced fuzzy c-means clustering algorithms are introduced, respectively. Finally, the Bayesian classifier using the Gaussian function with expectation maximization is used for DME grading. The accurate optic disc and fovea localization, exudates’ segmentation, and classification improved the overall system's performance. The proposed model achieves an average accuracy of 96.17%, 98.60%, 97.85%, and 98.80% for optic disc detection, fovea localization, exudates segmentation, and DME classification, respectively. The performance of the proposed model compared to the competitive studies illustrates the superiority of the suggested methodology.