P LALITHA SURYA KUMARI and V. Vijaya Madhavi
Adv. Artif. Intell. Mach. Learn., 6 (1):5092-5109
1. V. Vijaya Madhavi: Department of CSE, Koneru Lakshmaiah Education Foundation Hyderabad, Telangana, India.
2. P LALITHA SURYA KUMARI: Department of CSE, Koneru Lakshmaiah Education Foundation Hyderabad, Telangana, India
DOI: 10.54364/AAIML.2026.61283
Article History: Received on: 17-Nov-25, Accepted on: 19-Feb-26, Published on: 26-Feb-26
Corresponding Author: P LALITHA SURYA KUMARI
Email: vlalithanagesh@gmail.com
Citation: Vijaya Madhavi and P Lalitha Surya Kumari. Glaucoma Diagnosis via Advanced Retinal Image Processing and V-RACNet Classification. Advances in Artificial Intelligence and Machine Learning. 2026;6(1):283. https://dx.doi.org/10.54364/AAIML.2026.61283
The proposed paper presents a general
architecture of processing context in glaucoma diagnosis, entailing steps such
as image preprocessing to the classification with the new V-RACNet model. The
initial stages involve image resizing, removal of the green plane, and
enhancement of blood vessels extraction by morphological operations and
thinning. Focal regions and features extraction of the fovea regions including
GLCM texture features and statistical properties are identified through a clustering
algorithm (K-means). The glaucoma detector trained on samples of 1114 samples
of DRIVE database with 650 normal samples and 464 glaucomatous samples
(Low-Tension Glaucoma or Angle-Closure Glaucoma). The dataset is separated into two portions,
training (70%, 779 samples) and testing (30%, 335 samples) to determine the
model performance and ability to generalize its performance on new data.
Further on, a V-RACNet model is constructed, and trained on a labelled
database, and tested to classify glaucoma. The proposed method has remarkably
high accuracy (99.32%), sensitivity (99.42%), specificity (98.58%), and
precision (99.26) which indicates that it can be a powerful tool because of its
automatization capabilities in glaucoma diagnosis.