Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3803 2019 2019 Asst. Professor, Department of ECE, Madanapalle Institute of Technology & Science, MITS, Madanapalle, India Vivek Vivek Asst. Professor, Department of ECE, Madanapalle Institute of Technology & Science, MITS, Madanapalle, India H. Shree Kumar Department of Computer Science & Engineering, IES College of Technology, Bhopal, Madhya Pradesh, 462044, India Hemant Sharma Asst. Professor, Department of ECE, Madanapalle Institute of Technology & Science, MITS, Madanapalle, India R. Kiran Kumar Associate Professor, Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India Chandrasekaran Raja Asst. Professor, Department of CSE (Honors), Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India Krishna Kishore Thota The proposed research implements a new 3D-block-based alpha-rooting enhancement method, which uses PCA classification for detecting glaucoma. The use of Euclidean distance in current image enhancement methods tends to lose important structural details that result in incorrect classification outcomes. The proposed method executes block-matching and grouping operations to locate equivalent 3D patterns before using adaptive alpha-rooting adjustment, which automatically controls contrast throughout optic disc and optic cup regions. Following enhancement processing an additional polishing stage optimizes these results for classification purposes. The classification of enhanced images takes place using PCA and its wavelet variants to extract important retinal features. The proposed system utilizes both ACRIMA dataset and real-world hospital images to show better classification achievements than CLAHE-based enhancement while validating its effectiveness. The experimental outcome demonstrated both high accuracy and reduced time consumption when using biorthogonal DWT with (2D) ²-PCA for classification. The proposed method offers a time-effective hardware-oriented solution for automatic glaucoma detection by combining conventional statistical techniques with deep learning-based classification approaches. The method provides clinical facilities with a dependable standard for glaucoma identification and diagnosis improvement. The Proposed 3D block-based adaptive alpha rooting method achieves a total accuracy level of 95.1%. The U-net model achieves 91.0% accuracy while CNN reaches 90.3% and RF delivers 87.1%. At the same time, SVM provides 86.3% accuracy while PCA returns 85.2% and DWT reaches 84.2% and KNN establishes 81.2% accuracy. 2025 2025 177 195 10.54216/JISIoT.170113 https://www.americaspg.com/articleinfo/18/show/3803