Jahanzaib Latif¹, Shanshan Tu^1,*, Chuangbai Xiao¹, Anas Bilal², Sadaqat Ur Rehman³, Zohaib Ahmad⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1151-1172, 2023, DOI:10.32604/cmc.2023.040152

Abstract Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection is crucial to prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter (AMF) and feature extraction using the previously processed speeded-up robust feature (SURF), histogram of oriented gradients (HOG), and Global features. The enhanced Grey Wolf Optimization (GWO) technique is then employed… More >

Somasundaram Devaraj^*, Senthil Kumar Arunachalam

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1283-1299, 2023, DOI:10.32604/iasc.2023.033200

Abstract Retinal fundus images are used to discover many diseases. Several Machine learning algorithms are designed to identify the Glaucoma disease. But the accuracy and time consumption performance were not improved. To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regressive Segmentation based Radial Basis Image Classifier (MPCNKFTRS-RBIC) Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time. In MPCNKFTRS-RBIC Model, the retinal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuan filter. Then, preprocessed retinal fundus is given for hidden… More >

R. Praveena^*, T. R. GaneshBabu

Molecular & Cellular Biomechanics, Vol.18, No.2, pp. 69-86, 2021, DOI:10.32604/mcb.2021.014622

Abstract The cup nerve head, optic cup, optic disc ratio and neural rim configuration are observed as important for detecting glaucoma at an early stage in clinical practice. The main clinical indicator of glaucoma optic cup to disc ratio is currently determined manually by limiting the mass screening was potential. This paper proposes the following methods for an automatic cup to disc ratio determination. In the first part of the work, fundus image of the optic disc region is considered. Clustering means K is used automatically to extract the optic disc whereas K-value is automatically selected by algorithm called hill climbing.… More >