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Identifying the plus form of ROP, characterized by dilated and tortuous blood vessels, is crucial for timely intervention. This study introduces an intelligent segmentation\u2013classification system for the autonomous detection of retinal blood vessels and the classification of ROP plus form. Utilizing Clarity RetCam 3 images, our system employs morphological image processing and convolutional neural networks (CNNs) for segmentation and classification, respectively. Testing on a dataset of premature infants\u2019 retinal images demonstrates high segmentation accuracy (median\u2009=\u20090.974) and superior classification performance (accuracy\u2009=\u20090.975, sensitivity\u2009=\u20090.950, and specificity\u2009=\u20091). In addition, the system exhibits versatility, with successful segmentation in adult retinal images from public databases. These findings highlight the system\u2019s potential for clinical use in retinal vessel identification, feature extraction, and ROP plus form classification. The proposed system is capable of effectively identifying retinal blood vessels from both alternatives including adult and premature born retinal images with a high accuracy in contrast to related studies. 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