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The main factor behind pathological vessel growth results from retinal capillary depletion of oxygen that causes abnormal vascular development patterns. Early detection of these fundus image abnormalities requires precision because it enables ophthalmologists to provide effective treatment and make proper diagnoses. A multiple\u2010step image processing system treats this problem. A fusion\u2010based contrast enhancement method begins the process of enhancing diabetic retinopathy fundus image brightness and contrast. After the initial process, the system applies detail weighted histogram equalization to the green channel for better structural detail visualization. In the second stage, the proposed online tiger\u2010claw algorithm segments abnormal neovascularization from normal blood vessels. Next, the combination of fuzzy zone\u2010based clustering with optimization and classifier thresholding performs local identification along with highlight generation for neovascularized areas. Neovascularization detection makes use of a YOLOv5 neural network in the third stage through feature extraction and classification operations. A refined segmentation process occurs with the application of multistage gray wolf optimization. The proposed algorithm underwent testing through its application to the public datasets STARE, DRIVE, MESSIDOR, and DIARETDB1. Experimental tests indicate that the neovascularization region marking performed with 98.19% sensitivity and 96.56% specificity while reaching 99.27% accuracy. 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